#install.packages('TDAmapper')
library(TDAmapper)
library(cluster)
library(vip)
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#install.packages('kernlab’)
library(kernlab)
#install.packages(‘class’)
library(class)
#install.packages('nnet')
library(nnet)
#install.packages(‘randomForest’)
library(randomForest)
## randomForest 4.7-1.1
## Type rfNews() to see new features/changes/bug fixes.
#install.packages('e1071')
library(e1071)
#install.packages("BayesFactor")
library(BayesFactor)
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## ************
## Welcome to BayesFactor 0.9.12-4.5. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
##
## Type BFManual() to open the manual.
## ************
library(BayesPPD)
library(bayestestR)
#install.packages('igraph')
library('igraph')
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library(ggplot2)
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#install.packages('networkD3')
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library(rstanarm)
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## - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
## - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
## - For execution on a local, multicore CPU with excess RAM we recommend calling
## options(mc.cores = parallel::detectCores())
library(see)
#install.packages('tidyverse')
library(tidyverse)
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#install.packages('caret')
library(caret)
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library(MCMCpack)
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## ## Markov Chain Monte Carlo Package (MCMCpack)
## ## Copyright (C) 2003-2025 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
## ##
## ## Support provided by the U.S. National Science Foundation
## ## (Grants SES-0350646 and SES-0350613)
## ##
#linstall.packages("caret")
library(caret)
library(TDA)
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#install.packages('googledrive')
library(googledrive)
#install.packages('stringr')
library(stringr)
#install.packages('ks')
library(ks)
library(GGally)
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
#import DryBean dataset from UCI repository stored on my desktop
#Dry_Bean_Dataset **
library(readxl)
Dry_Bean_Dataset <- read_excel("~/Desktop/NCU/DissertationDatasets/DryBeanDataset/Dry_Bean_Dataset.xlsx")
head(str(Dry_Bean_Dataset))
## tibble [13,611 × 17] (S3: tbl_df/tbl/data.frame)
## $ Area : num [1:13611] 28395 28734 29380 30008 30140 ...
## $ Perimeter : num [1:13611] 610 638 624 646 620 ...
## $ MajorAxisLength: num [1:13611] 208 201 213 211 202 ...
## $ MinorAxisLength: num [1:13611] 174 183 176 183 190 ...
## $ AspectRation : num [1:13611] 1.2 1.1 1.21 1.15 1.06 ...
## $ Eccentricity : num [1:13611] 0.55 0.412 0.563 0.499 0.334 ...
## $ ConvexArea : num [1:13611] 28715 29172 29690 30724 30417 ...
## $ EquivDiameter : num [1:13611] 190 191 193 195 196 ...
## $ Extent : num [1:13611] 0.764 0.784 0.778 0.783 0.773 ...
## $ Solidity : num [1:13611] 0.989 0.985 0.99 0.977 0.991 ...
## $ roundness : num [1:13611] 0.958 0.887 0.948 0.904 0.985 ...
## $ Compactness : num [1:13611] 0.913 0.954 0.909 0.928 0.971 ...
## $ ShapeFactor1 : num [1:13611] 0.00733 0.00698 0.00724 0.00702 0.0067 ...
## $ ShapeFactor2 : num [1:13611] 0.00315 0.00356 0.00305 0.00321 0.00366 ...
## $ ShapeFactor3 : num [1:13611] 0.834 0.91 0.826 0.862 0.942 ...
## $ ShapeFactor4 : num [1:13611] 0.999 0.998 0.999 0.994 0.999 ...
## $ Class : chr [1:13611] "SEKER" "SEKER" "SEKER" "SEKER" ...
## NULL
summary(Dry_Bean_Dataset)
## Area Perimeter MajorAxisLength MinorAxisLength
## Min. : 20420 Min. : 524.7 Min. :183.6 Min. :122.5
## 1st Qu.: 36328 1st Qu.: 703.5 1st Qu.:253.3 1st Qu.:175.8
## Median : 44652 Median : 794.9 Median :296.9 Median :192.4
## Mean : 53048 Mean : 855.3 Mean :320.1 Mean :202.3
## 3rd Qu.: 61332 3rd Qu.: 977.2 3rd Qu.:376.5 3rd Qu.:217.0
## Max. :254616 Max. :1985.4 Max. :738.9 Max. :460.2
## AspectRation Eccentricity ConvexArea EquivDiameter
## Min. :1.025 Min. :0.2190 Min. : 20684 Min. :161.2
## 1st Qu.:1.432 1st Qu.:0.7159 1st Qu.: 36714 1st Qu.:215.1
## Median :1.551 Median :0.7644 Median : 45178 Median :238.4
## Mean :1.583 Mean :0.7509 Mean : 53768 Mean :253.1
## 3rd Qu.:1.707 3rd Qu.:0.8105 3rd Qu.: 62294 3rd Qu.:279.4
## Max. :2.430 Max. :0.9114 Max. :263261 Max. :569.4
## Extent Solidity roundness Compactness
## Min. :0.5553 Min. :0.9192 Min. :0.4896 Min. :0.6406
## 1st Qu.:0.7186 1st Qu.:0.9857 1st Qu.:0.8321 1st Qu.:0.7625
## Median :0.7599 Median :0.9883 Median :0.8832 Median :0.8013
## Mean :0.7497 Mean :0.9871 Mean :0.8733 Mean :0.7999
## 3rd Qu.:0.7869 3rd Qu.:0.9900 3rd Qu.:0.9169 3rd Qu.:0.8343
## Max. :0.8662 Max. :0.9947 Max. :0.9907 Max. :0.9873
## ShapeFactor1 ShapeFactor2 ShapeFactor3 ShapeFactor4
## Min. :0.002778 Min. :0.0005642 Min. :0.4103 Min. :0.9477
## 1st Qu.:0.005900 1st Qu.:0.0011535 1st Qu.:0.5814 1st Qu.:0.9937
## Median :0.006645 Median :0.0016935 Median :0.6420 Median :0.9964
## Mean :0.006564 Mean :0.0017159 Mean :0.6436 Mean :0.9951
## 3rd Qu.:0.007271 3rd Qu.:0.0021703 3rd Qu.:0.6960 3rd Qu.:0.9979
## Max. :0.010451 Max. :0.0036650 Max. :0.9748 Max. :0.9997
## Class
## Length:13611
## Class :character
## Mode :character
##
##
##
ggpairs(Dry_Bean_Dataset, aes(color = Class))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggpairs(Dry_Bean_Dataset, columns = c(1:8,17), aes(color = Class))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggpairs(Dry_Bean_Dataset, columns = c(9:17), aes(color = Class))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
##Add Bayesian tests functions
#create function to conduct the Bayesian Sign Test
BayesianSignTest <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 3000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
#for the moment we implement the sign test. Signedrank will follows
probLeft <- mean (diffVector < rope_min)
probRope <- mean (diffVector > rope_min & diffVector < rope_max)
probRight <- mean (diffVector > rope_max)
results = list ("probLeft"=probLeft, "probRope"=probRope,
"probRight"=probRight)
return (results)
}
##Create function to conduct Bayesian Signed Rank Test
BayesianSignedRank <- function(diffVector,rope_min,rope_max) {
library(MCMCpack)
samples <- 30000
#build the vector 0.5 1 1 ....... 1
weights <- c(0.5,rep(1,length(diffVector)))
#add the fake first observation in 0
diffVector <- c (0, diffVector)
sampledWeights <- rdirichlet(samples,weights)
winLeft <- vector(length = samples)
winRope <- vector(length = samples)
winRight <- vector(length = samples)
for (rep in 1:samples){
currentWeights <- sampledWeights[rep,]
for (i in 1:length(currentWeights)){
for (j in 1:length(currentWeights)){
product= currentWeights[i] * currentWeights[j]
if (diffVector[i]+diffVector[j] > (2*rope_max) ) {
winRight[rep] <- winRight[rep] + product
}
else if (diffVector[i]+diffVector[j] > (2*rope_min) ) {
winRope[rep] <- winRope[rep] + product
}
else {
winLeft[rep] <- winLeft[rep] + product
}
}
}
maxWins=max(winRight[rep],winRope[rep],winLeft[rep])
winners = (winRight[rep]==maxWins)*1 + (winRope[rep]==maxWins)*1 + (winLeft[rep]==maxWins)*1
winRight[rep] <- (winRight[rep]==maxWins)*1/winners
winRope[rep] <- (winRope[rep]==maxWins)*1/winners
winLeft[rep] <- (winLeft[rep]==maxWins)*1/winners
}
results = list ("winLeft"=mean(winLeft), "winRope"=mean(winRope),
"winRight"=mean(winRight) )
return (results)
}
#Create function to conduct the Bayesian Correlated t.test
#diff_a_b is a vector of differences between the two classifiers, on each fold of cross-validation.
#If you have done 10 runs of 10-folds cross-validation, you have 100 results for each classifier.
#You should have run cross-validation on the same folds for the two classifiers.
#Then diff_a_b is the difference fold-by-fold.
#rho is the correlation of the cross-validation results: 1/(number of folds)
#rope_min and rope_max are the lower and the upper bound of the rope
correlatedBayesianTtest <- function(diff_a_b,rho,rope_min,rope_max){
if (rope_max < rope_min){
stop("rope_max should be larger than rope_min")
}
delta <- mean(diff_a_b)
n <- length(diff_a_b)
df <- n-1
stdX <- sd(diff_a_b)
sp <- sd(diff_a_b)*sqrt(1/n + rho/(1-rho))
p.left <- pt((rope_min - delta)/sp, df)
p.rope <- pt((rope_max - delta)/sp, df)-p.left
results <- list('left'=p.left,'rope'=p.rope,'right'=1-p.left-p.rope)
return (results)
}
set.seed(16974)
###Prepare drybean dataset for One hot encoding if necessary and Persistent homology.
##One hot encoding for drybean dataset
library(caret)
#define one-hot encoding function
dummy_drybean <- dummyVars(" ~ .", data=Dry_Bean_Dataset)
#perform one-hot encoding on data frame
dry_bean_dataset_one_hot_df <- data.frame(predict(dummy_drybean, newdata=Dry_Bean_Dataset))
summary(dry_bean_dataset_one_hot_df)
## Area Perimeter MajorAxisLength MinorAxisLength
## Min. : 20420 Min. : 524.7 Min. :183.6 Min. :122.5
## 1st Qu.: 36328 1st Qu.: 703.5 1st Qu.:253.3 1st Qu.:175.8
## Median : 44652 Median : 794.9 Median :296.9 Median :192.4
## Mean : 53048 Mean : 855.3 Mean :320.1 Mean :202.3
## 3rd Qu.: 61332 3rd Qu.: 977.2 3rd Qu.:376.5 3rd Qu.:217.0
## Max. :254616 Max. :1985.4 Max. :738.9 Max. :460.2
## AspectRation Eccentricity ConvexArea EquivDiameter
## Min. :1.025 Min. :0.2190 Min. : 20684 Min. :161.2
## 1st Qu.:1.432 1st Qu.:0.7159 1st Qu.: 36714 1st Qu.:215.1
## Median :1.551 Median :0.7644 Median : 45178 Median :238.4
## Mean :1.583 Mean :0.7509 Mean : 53768 Mean :253.1
## 3rd Qu.:1.707 3rd Qu.:0.8105 3rd Qu.: 62294 3rd Qu.:279.4
## Max. :2.430 Max. :0.9114 Max. :263261 Max. :569.4
## Extent Solidity roundness Compactness
## Min. :0.5553 Min. :0.9192 Min. :0.4896 Min. :0.6406
## 1st Qu.:0.7186 1st Qu.:0.9857 1st Qu.:0.8321 1st Qu.:0.7625
## Median :0.7599 Median :0.9883 Median :0.8832 Median :0.8013
## Mean :0.7497 Mean :0.9871 Mean :0.8733 Mean :0.7999
## 3rd Qu.:0.7869 3rd Qu.:0.9900 3rd Qu.:0.9169 3rd Qu.:0.8343
## Max. :0.8662 Max. :0.9947 Max. :0.9907 Max. :0.9873
## ShapeFactor1 ShapeFactor2 ShapeFactor3 ShapeFactor4
## Min. :0.002778 Min. :0.0005642 Min. :0.4103 Min. :0.9477
## 1st Qu.:0.005900 1st Qu.:0.0011535 1st Qu.:0.5814 1st Qu.:0.9937
## Median :0.006645 Median :0.0016935 Median :0.6420 Median :0.9964
## Mean :0.006564 Mean :0.0017159 Mean :0.6436 Mean :0.9951
## 3rd Qu.:0.007271 3rd Qu.:0.0021703 3rd Qu.:0.6960 3rd Qu.:0.9979
## Max. :0.010451 Max. :0.0036650 Max. :0.9748 Max. :0.9997
## ClassBARBUNYA ClassBOMBAY ClassCALI ClassDERMASON
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.0000
## Mean :0.09713 Mean :0.03835 Mean :0.1198 Mean :0.2605
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## ClassHOROZ ClassSEKER ClassSIRA
## Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1417 Mean :0.1489 Mean :0.1937
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000
dry_bean_dataset_one_hot_1000_df <- dry_bean_dataset_one_hot_df[sample(nrow(dry_bean_dataset_one_hot_df), size = 1000, replace = FALSE), ]
head(str(dry_bean_dataset_one_hot_1000_df))
## 'data.frame': 1000 obs. of 23 variables:
## $ Area : num 95754 43864 22144 27940 53196 ...
## $ Perimeter : num 1182 799 558 615 905 ...
## $ MajorAxisLength: num 453 303 199 227 364 ...
## $ MinorAxisLength: num 273 184 143 157 187 ...
## $ AspectRation : num 1.66 1.65 1.39 1.45 1.95 ...
## $ Eccentricity : num 0.799 0.794 0.695 0.723 0.859 ...
## $ ConvexArea : num 97441 44336 22445 28256 53781 ...
## $ EquivDiameter : num 349 236 168 189 260 ...
## $ Extent : num 0.749 0.733 0.72 0.808 0.775 ...
## $ Solidity : num 0.983 0.989 0.987 0.989 0.989 ...
## $ roundness : num 0.861 0.863 0.895 0.929 0.817 ...
## $ Compactness : num 0.771 0.779 0.843 0.83 0.715 ...
## $ ShapeFactor1 : num 0.00473 0.00692 0.00899 0.00813 0.00685 ...
## $ ShapeFactor2 : num 0.00103 0.00157 0.0028 0.00238 0.0011 ...
## $ ShapeFactor3 : num 0.595 0.607 0.711 0.689 0.511 ...
## $ ShapeFactor4 : num 0.988 0.998 0.989 0.998 0.996 ...
## $ ClassBARBUNYA : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ClassBOMBAY : num 0 0 0 0 0 0 0 0 0 0 ...
## $ ClassCALI : num 1 0 0 0 0 1 0 0 0 1 ...
## $ ClassDERMASON : num 0 0 1 1 0 0 0 0 1 0 ...
## $ ClassHOROZ : num 0 0 0 0 1 0 0 0 0 0 ...
## $ ClassSEKER : num 0 0 0 0 0 0 0 1 0 0 ...
## $ ClassSIRA : num 0 1 0 0 0 0 1 0 0 0 ...
## NULL
##Persistent Homology of DryBean dataset
# calculate persistent homology for DryBean Dataset
phom_drybean_df <- calculate_homology(dry_bean_dataset_one_hot_1000_df)
# plot barcode for DryBean Dataset
plot_barcode(phom_drybean_df)
# plot persistent diagram of DryBean Dataset
plot_persist(phom_drybean_df)
#####———————————————MAPPER ALGORITHM————————————————
#Prepare Dry Bean dataset for Mapper 1D algorithm
##Two Filter Functions PCA & KDE
#Prepare linear PCA as a filter function by centering and scaling dataset first on all one hot df dataset
b<- prcomp(dry_bean_dataset_one_hot_df, center=TRUE, scale=TRUE)
ts_dry_bean_pca_b <- as.data.frame(predict(b, dry_bean_dataset_one_hot_df))
#Conduct kernel density estimator as a filter function on 4 of 6
filter.kde <- kde(dry_bean_dataset_one_hot_df[,1:4],H=diag(1,nrow = 4),eval.points = dry_bean_dataset_one_hot_df[,1:4])$estimate
##*** dry_bean_dataset Mapper 5 intervals, 60% overlap, 5 bins
m_dry_bean_dataset_5.60.5 <- mapper1D(
distance_matrix = dist(dry_bean_dataset_one_hot_df),
filter_values = c(ts_dry_bean_pca_b$PC1),
num_intervals = 5,
percent_overlap = 40,
num_bins_when_clustering = 5)
g_dry_bean_dataset_5.60.5 <- graph.adjacency(m_dry_bean_dataset_5.60.5$adjacency, mode="undirected")
## Warning: `graph.adjacency()` was deprecated in igraph 2.0.0.
## ℹ Please use `graph_from_adjacency_matrix()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
plot(g_dry_bean_dataset_5.60.5, layout = layout.auto(g_dry_bean_dataset_5.60.5))
## Warning: `layout.auto()` was deprecated in igraph 2.0.0.
## ℹ Please use `layout_nicely()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
head(str(m_dry_bean_dataset_5.60.5$level_of_vertex))
## int [1:5] 1 2 3 4 5
## NULL
head(str(m_dry_bean_dataset_5.60.5$vertices_in_level))
## List of 5
## $ : num 1
## $ : num 2
## $ : num 3
## $ : num 4
## $ : num 5
## NULL
head(str(m_dry_bean_dataset_5.60.5$points_in_vertex))
## List of 5
## $ : int [1:6835] 1 2 3 4 5 6 7 8 9 10 ...
## $ : int [1:8024] 272 279 431 433 457 646 667 713 759 798 ...
## $ : int [1:5008] 272 2028 2046 2054 2055 2056 2059 2060 2063 2064 ...
## $ : int [1:894] 2647 2935 2951 2987 3064 3066 3081 3082 3084 3093 ...
## $ : int [1:342] 3375 3424 3427 3428 3430 3435 3437 3450 3453 3456 ...
## NULL
my_resolution = 100
my_palette = colorRampPalette(c('red','green','lightblue'))
my_max = max(m_dry_bean_dataset_5.60.5$level_of_vertex, na.rm=TRUE)
my_vector = m_dry_bean_dataset_5.60.5$level_of_vertex / my_max
my_colors = my_palette(my_resolution)[as.numeric(cut(
my_vector, breaks=my_resolution))]
g_dry_bean_dataset_5.60.5 <- graph.adjacency(m_dry_bean_dataset_5.60.5$adjacency, mode="undirected")
vertex_size <- unlist(lapply(m_dry_bean_dataset_5.60.5$points_in_vertex,
function(x) length(x)))
plot(g_dry_bean_dataset_5.60.5, layout = layout.auto(g_dry_bean_dataset_5.60.5),
vertex.size = 30*log(vertex_size)/
max(log(vertex_size)),
vertex.color = my_colors)
m_dry_bean_dataset_5.60.5.n1<-m_dry_bean_dataset_5.60.5$points_in_vertex[1]
m_dry_bean_dataset_5.60.5.n1.vec<-as.vector(unlist(m_dry_bean_dataset_5.60.5.n1))
m_dry_bean_dataset_5.60.5.n2<-m_dry_bean_dataset_5.60.5$points_in_vertex[2]
m_dry_bean_dataset_5.60.5.n2.vec<-as.vector(unlist(m_dry_bean_dataset_5.60.5.n2))
m_dry_bean_dataset_5.60.5.n3<-m_dry_bean_dataset_5.60.5$points_in_vertex[3]
m_dry_bean_dataset_5.60.5.n3.vec<-as.vector(unlist(m_dry_bean_dataset_5.60.5.n3))
m_dry_bean_dataset_5.60.5.n4<-m_dry_bean_dataset_5.60.5$points_in_vertex[4]
m_dry_bean_dataset_5.60.5.n4.vec<-as.vector(unlist(m_dry_bean_dataset_5.60.5.n4))
m_dry_bean_dataset_5.60.5.n5<-m_dry_bean_dataset_5.60.5$points_in_vertex[5]
m_dry_bean_dataset_5.60.5.n5.vec<-as.vector(unlist(m_dry_bean_dataset_5.60.5.n5))
##map the ID’s of each Mapper vertex point to the actual dry_bean_dataset One Hot DF1 dataset
tda.m_dry_bean_dataset_5.60.5.n1.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.60.5.n1.vec,]
tda.m_dry_bean_dataset_5.60.5.n2.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.60.5.n2.vec,]
tda.m_dry_bean_dataset_5.60.5.n3.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.60.5.n3.vec,]
tda.m_dry_bean_dataset_5.60.5.n4.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.60.5.n4.vec,]
tda.m_dry_bean_dataset_5.60.5.n5.vec<-Dry_Bean_Dataset[m_dry_bean_dataset_5.60.5.n5.vec,]
##*** dry_bean_dataset Mapper KDE 5 intervals, 60% overlap, 5 bins
m_kde_dry_bean_dataset_5.60.5 <- mapper1D(
distance_matrix = dist(dry_bean_dataset_one_hot_df),
filter_values = c(filter.kde),
num_intervals = 5,
percent_overlap = 40,
num_bins_when_clustering = 5)
g_kde_dry_bean_dataset_5.60.5 <- graph.adjacency(m_kde_dry_bean_dataset_5.60.5$adjacency, mode="undirected")
plot(g_kde_dry_bean_dataset_5.60.5, layout = layout.auto(g_kde_dry_bean_dataset_5.60.5))
head(str(m_kde_dry_bean_dataset_5.60.5$level_of_vertex))
## int [1:5] 1 2 3 4 5
## NULL
head(str(m_kde_dry_bean_dataset_5.60.5$vertices_in_level))
## List of 5
## $ : num 1
## $ : num 2
## $ : num 3
## $ : num 4
## $ : num 5
## NULL
head(str(m_kde_dry_bean_dataset_5.60.5$points_in_vertex))
## List of 5
## $ : int [1:7503] 1 2 3 4 5 6 7 8 9 10 ...
## $ : int [1:7002] 1 3 4 6 8 9 10 11 13 14 ...
## $ : int [1:3511] 25 108 159 183 197 198 202 206 209 211 ...
## $ : int [1:1759] 294 369 374 376 401 402 409 413 431 433 ...
## $ : int [1:774] 548 593 615 616 618 631 633 638 640 646 ...
## NULL
my_resolution = 100
my_palette = colorRampPalette(c('red','green','lightblue'))
my_max = max(m_kde_dry_bean_dataset_5.60.5$level_of_vertex, na.rm=TRUE)
my_vector = m_kde_dry_bean_dataset_5.60.5$level_of_vertex / my_max
my_colors = my_palette(my_resolution)[as.numeric(cut(
my_vector, breaks=my_resolution))]
g_kde_dry_bean_dataset_5.60.5 <- graph.adjacency(m_kde_dry_bean_dataset_5.60.5$adjacency, mode="undirected")
vertex_size <- unlist(lapply(m_kde_dry_bean_dataset_5.60.5$points_in_vertex,
function(x) length(x)))
plot(g_kde_dry_bean_dataset_5.60.5, layout = layout.auto(g_kde_dry_bean_dataset_5.60.5),
vertex.size = 30*log(vertex_size)/
max(log(vertex_size)),
vertex.color = my_colors)
##Extract the ID observations of each mapper output vertex
m_kde_dry_bean_dataset_5.60.5.n1<-m_kde_dry_bean_dataset_5.60.5$points_in_vertex[1]
m_kde_dry_bean_dataset_5.60.5.n1.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.60.5.n1))
m_kde_dry_bean_dataset_5.60.5.n2<-m_kde_dry_bean_dataset_5.60.5$points_in_vertex[2]
m_kde_dry_bean_dataset_5.60.5.n2.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.60.5.n2))
m_kde_dry_bean_dataset_5.60.5.n3<-m_kde_dry_bean_dataset_5.60.5$points_in_vertex[3]
m_kde_dry_bean_dataset_5.60.5.n3.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.60.5.n3))
m_kde_dry_bean_dataset_5.60.5.n4<-m_kde_dry_bean_dataset_5.60.5$points_in_vertex[4]
m_kde_dry_bean_dataset_5.60.5.n4.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.60.5.n4))
m_kde_dry_bean_dataset_5.60.5.n5<-m_kde_dry_bean_dataset_5.60.5 $points_in_vertex[5]
m_kde_dry_bean_dataset_5.60.5.n5.vec<-as.vector(unlist(m_kde_dry_bean_dataset_5.60.5.n5))
##map the ID’s of each Mapper vertex point to the actual dry_bean_dataset One Hot DF4 dataset
tda.m_kde_dry_bean_dataset_5.60.5.n1.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.60.5.n1.vec,]
tda.m_kde_dry_bean_dataset_5.60.5.n2.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.60.5.n2.vec,]
tda.m_kde_dry_bean_dataset_5.60.5.n3.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.60.5.n3.vec,]
tda.m_kde_dry_bean_dataset_5.60.5.n4.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.60.5.n4.vec,]
tda.m_kde_dry_bean_dataset_5.60.5.n5.vec<-Dry_Bean_Dataset[m_kde_dry_bean_dataset_5.60.5.n5.vec,]
library(caret)
trainIndex <- createDataPartition(Dry_Bean_Dataset$Class, p = .7,
list = FALSE,
times = 1)
head(trainIndex)
## Resample1
## [1,] 1
## [2,] 3
## [3,] 4
## [4,] 5
## [5,] 7
## [6,] 8
Dry_Bean_DatasetTrain <- Dry_Bean_Dataset[ trainIndex,]
Dry_Bean_DatasetTest <- Dry_Bean_Dataset[-trainIndex,]
#Train Control: k-Fold Cross-validation basis for all models
fitControl <- trainControl(## 10-fold CV
method = "cv",
number = 3)
#Non-TDA-Assited
rfGrid<-expand.grid(mtry = (1:20)*50)
#Random Forest
dryBeanRfFit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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dryBeanRfFit
## Random Forest
##
## 9531 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 6354, 6354, 6354
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9199454 0.9031325
## 100 0.9195258 0.9026320
## 150 0.9198405 0.9030070
## 200 0.9192110 0.9022480
## 250 0.9191061 0.9021262
## 300 0.9192110 0.9022501
## 350 0.9193159 0.9023746
## 400 0.9191061 0.9021176
## 450 0.9196307 0.9027497
## 500 0.9196307 0.9027488
## 550 0.9194208 0.9024972
## 600 0.9186864 0.9016126
## 650 0.9191061 0.9021327
## 700 0.9193159 0.9023720
## 750 0.9204700 0.9037695
## 800 0.9203651 0.9036391
## 850 0.9192110 0.9022435
## 900 0.9195258 0.9026241
## 950 0.9196307 0.9027567
## 1000 0.9188962 0.9018679
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 750.
dryBeanRfFit$resample
## Accuracy Kappa Resample
## 1 0.9222537 0.9059559 Fold1
## 2 0.9257161 0.9101383 Fold3
## 3 0.9134404 0.8952144 Fold2
db_rf_fit_re<-dryBeanRfFit$resample[1]
summary(dryBeanRfFit)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 9531 factor numeric
## err.rate 4000 -none- numeric
## confusion 56 -none- numeric
## votes 66717 matrix numeric
## oob.times 9531 -none- numeric
## classes 7 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 9531 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 7 -none- character
## param 1 -none- list
vip(dryBeanRfFit,25) + ggtitle("non-TDA-Assisted: RF")
# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanRfFit, newdata = Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_rf_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_rf_cf
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 344 0 15 0 2 5 3
## BOMBAY 1 156 0 0 0 0 0
## CALI 28 0 456 0 12 0 1
## DERMASON 0 0 0 997 5 17 69
## HOROZ 6 0 12 2 552 0 14
## SEKER 5 0 2 11 0 575 13
## SIRA 12 0 4 53 7 11 690
##
## Overall Statistics
##
## Accuracy : 0.924
## 95% CI : (0.9155, 0.932)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.908
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.86869 1.00000 0.9325 0.9379
## Specificity 0.99321 0.99975 0.9886 0.9698
## Pos Pred Value 0.93225 0.99363 0.9175 0.9164
## Neg Pred Value 0.98599 1.00000 0.9908 0.9779
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08431 0.03824 0.1118 0.2444
## Detection Prevalence 0.09044 0.03848 0.1218 0.2667
## Balanced Accuracy 0.93095 0.99987 0.9605 0.9539
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9550 0.9457 0.8734
## Specificity 0.9903 0.9911 0.9736
## Pos Pred Value 0.9420 0.9488 0.8880
## Neg Pred Value 0.9926 0.9905 0.9697
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1353 0.1409 0.1691
## Detection Prevalence 0.1436 0.1485 0.1904
## Balanced Accuracy 0.9727 0.9684 0.9235
db_rf_cf$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9240196 0.9080495 0.9154592 0.9319667 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_rf_cf_ov_acc<-db_rf_cf$overall[1]
db_rf_cf$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8686869 0.9932139 0.9322493 0.9859876 0.9322493
## Class: BOMBAY 1.0000000 0.9997452 0.9936306 1.0000000 0.9936306
## Class: CALI 0.9325153 0.9885826 0.9175050 0.9907898 0.9175050
## Class: DERMASON 0.9379116 0.9698376 0.9163603 0.9779412 0.9163603
## Class: HOROZ 0.9550173 0.9902913 0.9419795 0.9925587 0.9419795
## Class: SEKER 0.9457237 0.9910714 0.9488449 0.9905009 0.9488449
## Class: SIRA 0.8734177 0.9735562 0.8880309 0.9697245 0.8880309
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8686869 0.8993464 0.09705882 0.08431373
## Class: BOMBAY 1.0000000 0.9968051 0.03823529 0.03823529
## Class: CALI 0.9325153 0.9249493 0.11985294 0.11176471
## Class: DERMASON 0.9379116 0.9270107 0.26053922 0.24436275
## Class: HOROZ 0.9550173 0.9484536 0.14166667 0.13529412
## Class: SEKER 0.9457237 0.9472817 0.14901961 0.14093137
## Class: SIRA 0.8734177 0.8806637 0.19362745 0.16911765
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09044118 0.9309504
## Class: BOMBAY 0.03848039 0.9998726
## Class: CALI 0.12181373 0.9605490
## Class: DERMASON 0.26666667 0.9538746
## Class: HOROZ 0.14362745 0.9726543
## Class: SEKER 0.14852941 0.9683976
## Class: SIRA 0.19044118 0.9234870
db_rf_cf_pre_rec_f1<-db_rf_cf$byClass[5:7]
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_PC_5.60.5_n1_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n1.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
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## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.60.5_n1_RfFit0
## Random Forest
##
## 6835 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4556, 4557, 4557
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9073882 0.8474409
## 100 0.9070956 0.8471241
## 150 0.9078270 0.8482150
## 200 0.9066566 0.8462709
## 250 0.9073888 0.8476041
## 300 0.9069493 0.8468482
## 350 0.9076809 0.8480734
## 400 0.9066569 0.8463018
## 450 0.9057786 0.8449853
## 500 0.9076810 0.8481543
## 550 0.9066567 0.8463483
## 600 0.9072421 0.8472366
## 650 0.9060714 0.8453521
## 700 0.9060716 0.8454251
## 750 0.9072419 0.8473494
## 800 0.9075346 0.8478233
## 850 0.9072419 0.8471324
## 900 0.9066569 0.8463292
## 950 0.9065102 0.8460345
## 1000 0.9075347 0.8477997
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 150.
DryBean_TDA_PC_5.60.5_n1_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.9086918 0.8490493 Fold2
## 2 0.9104871 0.8531404 Fold1
## 3 0.9043020 0.8424554 Fold3
db_tda_pc_5.60.5_n1_rf_fit0_re<-DryBean_TDA_PC_5.60.5_n1_RfFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n1_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 6835 factor numeric
## err.rate 3500 -none- numeric
## confusion 42 -none- numeric
## votes 41010 matrix numeric
## oob.times 6835 -none- numeric
## classes 6 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 6835 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 6 -none- character
## param 1 -none- list
vip(DryBean_TDA_PC_5.60.5_n1_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n1_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_PC_5.60.5_n1_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n1_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n1_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n1_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 12 1 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 1 0 0 0 0
## DERMASON 0 0 0 1062 226 1 30
## HOROZ 0 0 0 0 1 0 0
## SEKER 363 151 486 0 269 607 210
## SIRA 21 4 2 1 82 0 550
##
## Overall Statistics
##
## Accuracy : 0.5473
## 95% CI : (0.5319, 0.5627)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4397
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.030303 0.00000 0.0020450 0.9991
## Specificity 0.999729 1.00000 1.0000000 0.9148
## Pos Pred Value 0.923077 NaN 1.0000000 0.8052
## Neg Pred Value 0.905582 0.96176 0.8803628 0.9996
## Prevalence 0.097059 0.03824 0.1198529 0.2605
## Detection Rate 0.002941 0.00000 0.0002451 0.2603
## Detection Prevalence 0.003186 0.00000 0.0002451 0.3233
## Balanced Accuracy 0.515016 0.50000 0.5010225 0.9569
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0017301 0.9984 0.6962
## Specificity 1.0000000 0.5740 0.9666
## Pos Pred Value 1.0000000 0.2910 0.8333
## Neg Pred Value 0.8585438 0.9995 0.9298
## Prevalence 0.1416667 0.1490 0.1936
## Detection Rate 0.0002451 0.1488 0.1348
## Detection Prevalence 0.0002451 0.5113 0.1618
## Balanced Accuracy 0.5008651 0.7862 0.8314
db_tda_pc_5.60.5_n1_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 12 1 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 1 0 0 0 0
## DERMASON 0 0 0 1062 226 1 30
## HOROZ 0 0 0 0 1 0 0
## SEKER 363 151 486 0 269 607 210
## SIRA 21 4 2 1 82 0 550
##
## Overall Statistics
##
## Accuracy : 0.5473
## 95% CI : (0.5319, 0.5627)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4397
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.030303 0.00000 0.0020450 0.9991
## Specificity 0.999729 1.00000 1.0000000 0.9148
## Pos Pred Value 0.923077 NaN 1.0000000 0.8052
## Neg Pred Value 0.905582 0.96176 0.8803628 0.9996
## Prevalence 0.097059 0.03824 0.1198529 0.2605
## Detection Rate 0.002941 0.00000 0.0002451 0.2603
## Detection Prevalence 0.003186 0.00000 0.0002451 0.3233
## Balanced Accuracy 0.515016 0.50000 0.5010225 0.9569
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0017301 0.9984 0.6962
## Specificity 1.0000000 0.5740 0.9666
## Pos Pred Value 1.0000000 0.2910 0.8333
## Neg Pred Value 0.8585438 0.9995 0.9298
## Prevalence 0.1416667 0.1490 0.1936
## Detection Rate 0.0002451 0.1488 0.1348
## Detection Prevalence 0.0002451 0.5113 0.1618
## Balanced Accuracy 0.5008651 0.7862 0.8314
db_tda_pc_5.60.5_n1_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5473039 0.4396538 0.5318787 0.5626614 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n1_rf_cf0_ov_acc<-db_tda_pc_5.60.5_n1_rf_cf0$overall[1]
db_tda_pc_5.60.5_n1_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.030303030 0.9997286 0.9230769 0.9055815 0.9230769
## Class: BOMBAY 0.000000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.002044990 1.0000000 1.0000000 0.8803628 1.0000000
## Class: DERMASON 0.999059266 0.9148160 0.8051554 0.9996378 0.8051554
## Class: HOROZ 0.001730104 1.0000000 1.0000000 0.8585438 1.0000000
## Class: SEKER 0.998355263 0.5740207 0.2909875 0.9994985 0.2909875
## Class: SIRA 0.696202532 0.9665653 0.8333333 0.9298246 0.8333333
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.030303030 0.058679707 0.09705882 0.002941176
## Class: BOMBAY 0.000000000 NA 0.03823529 0.000000000
## Class: CALI 0.002044990 0.004081633 0.11985294 0.000245098
## Class: DERMASON 0.999059266 0.891687657 0.26053922 0.260294118
## Class: HOROZ 0.001730104 0.003454231 0.14166667 0.000245098
## Class: SEKER 0.998355263 0.450631032 0.14901961 0.148774510
## Class: SIRA 0.696202532 0.758620690 0.19362745 0.134803922
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.003186275 0.5150158
## Class: BOMBAY 0.000000000 0.5000000
## Class: CALI 0.000245098 0.5010225
## Class: DERMASON 0.323284314 0.9569377
## Class: HOROZ 0.000245098 0.5008651
## Class: SEKER 0.511274510 0.7861880
## Class: SIRA 0.161764706 0.8313839
db_tda_pc_5.60.5_n1_rf_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n1_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_rf_n1_3_fold<-(db_rf_fit_re-db_tda_pc_5.60.5_n1_rf_fit0_re)
diff_drybean_tda_pca_5.60.5_rf_n1_3_fold
## Accuracy
## 1 0.013561864
## 2 0.015229029
## 3 0.009138333
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n1_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_rf.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_rf.n1_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.3092
##
## $winRight
## [1] 0.6908
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n1_3_fold
## $left
## [1] 0.004239549
##
## $rope
## [1] 0.1632107
##
## $right
## [1] 0.8325498
# Rope Plot
plot(rope(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold),c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold))
#bf_tda_pca_5.60.5_rf.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_rf_n1_3_fold)
## t = 6.9572, df = 2, p-value = 0.02004
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.004824048 0.020462102
## sample estimates:
## mean of x
## 0.01264308
### Test set diff
diff_drybean_tda_pca_5.60.5_rf.n1_test<-(db_rf_cf_ov_acc-db_tda_pc_5.60.5_n1_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_rf.n1_test
## Accuracy
## 0.3767157
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n1_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n1_test$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n1_test$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_rf.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_rf.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1589
##
## $winRight
## [1] 0.8411
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_rf.n1_test)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf.n1_test)) #bf_tda_pca_5.60.5_rf.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n1_test))
##Node2
DryBean_TDA_PC_5.60.5_n2_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n2.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.60.5_n2_RfFit0
## Random Forest
##
## 8024 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5349, 5350, 5349
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.8849708 0.8501869
## 100 0.8867155 0.8524533
## 150 0.8845966 0.8496950
## 200 0.8857183 0.8511796
## 250 0.8862168 0.8518103
## 300 0.8855937 0.8510037
## 350 0.8850952 0.8503930
## 400 0.8863416 0.8519450
## 450 0.8864661 0.8521268
## 500 0.8853446 0.8506606
## 550 0.8840984 0.8490371
## 600 0.8860923 0.8516366
## 650 0.8863414 0.8519889
## 700 0.8864664 0.8521416
## 750 0.8838489 0.8487549
## 800 0.8854693 0.8508190
## 850 0.8849707 0.8501588
## 900 0.8862169 0.8517975
## 950 0.8872141 0.8531025
## 1000 0.8858430 0.8513187
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 950.
DryBean_TDA_PC_5.60.5_n2_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.8829907 0.8475219 Fold1
## 2 0.8852336 0.8506156 Fold3
## 3 0.8934181 0.8611702 Fold2
db_tda_pc_5.60.5_n2_rf_fit0_re<-DryBean_TDA_PC_5.60.5_n2_RfFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n2_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 8024 factor numeric
## err.rate 3500 -none- numeric
## confusion 42 -none- numeric
## votes 48144 matrix numeric
## oob.times 8024 -none- numeric
## classes 6 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 8024 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 6 -none- character
## param 1 -none- list
vip(DryBean_TDA_PC_5.60.5_n2_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n2_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_PC_5.60.5_n2_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n2_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n2_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n2_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 390 114 78 0 2 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 5 42 407 0 18 0 0
## DERMASON 0 0 0 1014 0 105 0
## HOROZ 1 0 4 0 471 0 0
## SEKER 0 0 0 0 0 277 0
## SIRA 0 0 0 49 87 226 790
##
## Overall Statistics
##
## Accuracy : 0.8208
## 95% CI : (0.8087, 0.8325)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7814
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98485 0.00000 0.83231 0.9539
## Specificity 0.94734 1.00000 0.98190 0.9652
## Pos Pred Value 0.66781 NaN 0.86229 0.9062
## Neg Pred Value 0.99828 0.96176 0.97727 0.9835
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.09559 0.00000 0.09975 0.2485
## Detection Prevalence 0.14314 0.00000 0.11569 0.2743
## Balanced Accuracy 0.96609 0.50000 0.90711 0.9596
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8149 0.45559 1.0000
## Specificity 0.9986 1.00000 0.8900
## Pos Pred Value 0.9895 1.00000 0.6858
## Neg Pred Value 0.9703 0.91296 1.0000
## Prevalence 0.1417 0.14902 0.1936
## Detection Rate 0.1154 0.06789 0.1936
## Detection Prevalence 0.1167 0.06789 0.2824
## Balanced Accuracy 0.9067 0.72780 0.9450
db_tda_pc_5.60.5_n2_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 390 114 78 0 2 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 5 42 407 0 18 0 0
## DERMASON 0 0 0 1014 0 105 0
## HOROZ 1 0 4 0 471 0 0
## SEKER 0 0 0 0 0 277 0
## SIRA 0 0 0 49 87 226 790
##
## Overall Statistics
##
## Accuracy : 0.8208
## 95% CI : (0.8087, 0.8325)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.7814
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98485 0.00000 0.83231 0.9539
## Specificity 0.94734 1.00000 0.98190 0.9652
## Pos Pred Value 0.66781 NaN 0.86229 0.9062
## Neg Pred Value 0.99828 0.96176 0.97727 0.9835
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.09559 0.00000 0.09975 0.2485
## Detection Prevalence 0.14314 0.00000 0.11569 0.2743
## Balanced Accuracy 0.96609 0.50000 0.90711 0.9596
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8149 0.45559 1.0000
## Specificity 0.9986 1.00000 0.8900
## Pos Pred Value 0.9895 1.00000 0.6858
## Neg Pred Value 0.9703 0.91296 1.0000
## Prevalence 0.1417 0.14902 0.1936
## Detection Rate 0.1154 0.06789 0.1936
## Detection Prevalence 0.1167 0.06789 0.2824
## Balanced Accuracy 0.9067 0.72780 0.9450
db_tda_pc_5.60.5_n2_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8208333 0.7813625 0.8087152 0.8324902 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n2_rf_cf0_ov_acc<-db_tda_pc_5.60.5_n2_rf_cf0$overall[1]
db_tda_pc_5.60.5_n2_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.9848485 0.9473398 0.6678082 0.9982838 0.6678082
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.8323108 0.9818992 0.8622881 0.9772727 0.8622881
## Class: DERMASON 0.9539040 0.9651972 0.9061662 0.9834515 0.9061662
## Class: HOROZ 0.8148789 0.9985722 0.9894958 0.9703108 0.9894958
## Class: SEKER 0.4555921 1.0000000 1.0000000 0.9129634 1.0000000
## Class: SIRA 1.0000000 0.8899696 0.6857639 1.0000000 0.6857639
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9848485 0.7959184 0.09705882 0.09558824
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.8323108 0.8470343 0.11985294 0.09975490
## Class: DERMASON 0.9539040 0.9294225 0.26053922 0.24852941
## Class: HOROZ 0.8148789 0.8937381 0.14166667 0.11544118
## Class: SEKER 0.4555921 0.6259887 0.14901961 0.06789216
## Class: SIRA 1.0000000 0.8135942 0.19362745 0.19362745
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.14313725 0.9660942
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.11568627 0.9071050
## Class: DERMASON 0.27426471 0.9595506
## Class: HOROZ 0.11666667 0.9067256
## Class: SEKER 0.06789216 0.7277961
## Class: SIRA 0.28235294 0.9449848
db_tda_pc_5.60.5_n2_rf_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n2_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_rf_n2_3_fold<-(db_rf_fit_re-db_tda_pc_5.60.5_n2_rf_fit0_re)
diff_drybean_tda_pca_5.60.5_rf_n2_3_fold
## Accuracy
## 1 0.03926304
## 2 0.04048244
## 3 0.02002225
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n2_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_rf.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_rf.n2_3_fold
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n2_3_fold
## $left
## [1] 0.01494601
##
## $rope
## [1] 0.03172187
##
## $right
## [1] 0.9533321
# Rope Plot
#plot(rope(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold),c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold))
#bf_tda_pca_5.60.5_rf.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_rf_n2_3_fold)
## t = 5.0189, df = 2, p-value = 0.03748
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.004745733 0.061766092
## sample estimates:
## mean of x
## 0.03325591
### Test set diff
diff_drybean_tda_pca_5.60.5_rf.n2_test<-(db_rf_cf_ov_acc-db_tda_pc_5.60.5_n2_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_rf.n2_test
## Accuracy
## 0.1031863
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n2_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n2_test$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n2_test$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_rf.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_rf.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1557667
##
## $winRight
## [1] 0.8442333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test),c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test)) #bf_tda_pca_5.60.5_rf.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test))
##Node3
DryBean_TDA_PC_5.60.5_n3_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n3.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 50 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 100 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 150 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 200 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 250 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 300 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 350 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 400 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 450 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 500 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 550 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 600 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 650 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 700 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 750 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 800 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 850 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 900 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 950 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry=1000 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.60.5_n3_RfFit0
## Random Forest
##
## 5008 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 3339, 3338, 3339
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9367885 0.9082584
## 100 0.9361893 0.9074201
## 150 0.9370881 0.9087076
## 200 0.9373877 0.9091516
## 250 0.9355902 0.9065185
## 300 0.9364889 0.9078622
## 350 0.9370881 0.9087224
## 400 0.9373877 0.9091014
## 450 0.9367885 0.9082479
## 500 0.9376872 0.9095814
## 550 0.9364889 0.9078450
## 600 0.9376872 0.9095792
## 650 0.9373877 0.9091605
## 700 0.9367885 0.9082824
## 750 0.9367885 0.9082737
## 800 0.9352906 0.9061078
## 850 0.9364889 0.9078127
## 900 0.9364889 0.9078751
## 950 0.9355902 0.9065339
## 1000 0.9358898 0.9069825
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 500.
DryBean_TDA_PC_5.60.5_n3_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.9370881 0.9085753 Fold1
## 2 0.9382864 0.9105876 Fold3
## 3 NA NA Fold2
db_tda_pc_5.60.5_n3_rf_fit0_re<-DryBean_TDA_PC_5.60.5_n3_RfFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n3_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 5008 factor numeric
## err.rate 4000 -none- numeric
## confusion 56 -none- numeric
## votes 35056 matrix numeric
## oob.times 5008 -none- numeric
## classes 7 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 5008 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 7 -none- character
## param 1 -none- list
vip(DryBean_TDA_PC_5.60.5_n3_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n3_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_PC_5.60.5_n3_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n3_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n3_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.60.5_n3_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 389 3 0 9 0 29 22
## BOMBAY 0 90 0 0 0 0 0
## CALI 1 63 487 0 0 154 4
## DERMASON 0 0 0 2 0 0 0
## HOROZ 2 0 2 1048 578 421 527
## SEKER 0 0 0 0 0 1 0
## SIRA 4 0 0 4 0 3 237
##
## Overall Statistics
##
## Accuracy : 0.4373
## 95% CI : (0.422, 0.4526)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3503
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98232 0.57692 0.9959 0.0018815
## Specificity 0.98290 1.00000 0.9382 1.0000000
## Pos Pred Value 0.86062 1.00000 0.6869 1.0000000
## Neg Pred Value 0.99807 0.98346 0.9994 0.7398234
## Prevalence 0.09706 0.03824 0.1199 0.2605392
## Detection Rate 0.09534 0.02206 0.1194 0.0004902
## Detection Prevalence 0.11078 0.02206 0.1738 0.0004902
## Balanced Accuracy 0.98261 0.78846 0.9670 0.5009407
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 1.0000 0.0016447 0.30000
## Specificity 0.4289 1.0000000 0.99666
## Pos Pred Value 0.2242 1.0000000 0.95565
## Neg Pred Value 1.0000 0.8511890 0.85569
## Prevalence 0.1417 0.1490196 0.19363
## Detection Rate 0.1417 0.0002451 0.05809
## Detection Prevalence 0.6319 0.0002451 0.06078
## Balanced Accuracy 0.7144 0.5008224 0.64833
db_tda_pc_5.60.5_n3_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 389 3 0 9 0 29 22
## BOMBAY 0 90 0 0 0 0 0
## CALI 1 63 487 0 0 154 4
## DERMASON 0 0 0 2 0 0 0
## HOROZ 2 0 2 1048 578 421 527
## SEKER 0 0 0 0 0 1 0
## SIRA 4 0 0 4 0 3 237
##
## Overall Statistics
##
## Accuracy : 0.4373
## 95% CI : (0.422, 0.4526)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3503
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98232 0.57692 0.9959 0.0018815
## Specificity 0.98290 1.00000 0.9382 1.0000000
## Pos Pred Value 0.86062 1.00000 0.6869 1.0000000
## Neg Pred Value 0.99807 0.98346 0.9994 0.7398234
## Prevalence 0.09706 0.03824 0.1199 0.2605392
## Detection Rate 0.09534 0.02206 0.1194 0.0004902
## Detection Prevalence 0.11078 0.02206 0.1738 0.0004902
## Balanced Accuracy 0.98261 0.78846 0.9670 0.5009407
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 1.0000 0.0016447 0.30000
## Specificity 0.4289 1.0000000 0.99666
## Pos Pred Value 0.2242 1.0000000 0.95565
## Neg Pred Value 1.0000 0.8511890 0.85569
## Prevalence 0.1417 0.1490196 0.19363
## Detection Rate 0.1417 0.0002451 0.05809
## Detection Prevalence 0.6319 0.0002451 0.06078
## Balanced Accuracy 0.7144 0.5008224 0.64833
db_tda_pc_5.60.5_n3_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.372549e-01 3.502756e-01 4.219610e-01 4.526388e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 3.328936e-131 NaN
db_tda_pc_5.60.5_n3_rf_cf0_ov_acc<-db_tda_pc_5.60.5_n3_rf_cf0$overall[1]
db_tda_pc_5.60.5_n3_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.982323232 0.9828990 0.8606195 0.9980706 0.8606195
## Class: BOMBAY 0.576923077 1.0000000 1.0000000 0.9834586 1.0000000
## Class: CALI 0.995910020 0.9381788 0.6868829 0.9994067 0.6868829
## Class: DERMASON 0.001881468 1.0000000 1.0000000 0.7398234 1.0000000
## Class: HOROZ 1.000000000 0.4288978 0.2242048 1.0000000 0.2242048
## Class: SEKER 0.001644737 1.0000000 1.0000000 0.8511890 1.0000000
## Class: SIRA 0.300000000 0.9966565 0.9556452 0.8556889 0.9556452
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.982323232 0.917452830 0.09705882 0.0953431373
## Class: BOMBAY 0.576923077 0.731707317 0.03823529 0.0220588235
## Class: CALI 0.995910020 0.813021703 0.11985294 0.1193627451
## Class: DERMASON 0.001881468 0.003755869 0.26053922 0.0004901961
## Class: HOROZ 1.000000000 0.366286439 0.14166667 0.1416666667
## Class: SEKER 0.001644737 0.003284072 0.14901961 0.0002450980
## Class: SIRA 0.300000000 0.456647399 0.19362745 0.0580882353
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.1107843137 0.9826111
## Class: BOMBAY 0.0220588235 0.7884615
## Class: CALI 0.1737745098 0.9670444
## Class: DERMASON 0.0004901961 0.5009407
## Class: HOROZ 0.6318627451 0.7144489
## Class: SEKER 0.0002450980 0.5008224
## Class: SIRA 0.0607843137 0.6483283
db_tda_pc_5.60.5_n3_rf_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n3_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_rf_n3_3_fold<-(db_rf_fit_re-db_tda_pc_5.60.5_n3_rf_fit0_re)
diff_drybean_tda_pca_5.60.5_rf_n3_3_fold
## Accuracy
## 1 -0.01483438
## 2 -0.01257031
## 3 NA
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n3_3_fold
## $probLeft
## [1] NA
##
## $probRope
## [1] NA
##
## $probRight
## [1] NA
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n3_3_fold_odds.left
## [1] NA
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_rf.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n3_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_rf.n3_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_rf.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n3_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_rf.n2_3_fold
# Rope Plot
#plot(rope(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n3_3_fold),c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf_n3_3_fold))
#bf_tda_pca_5.60.5_rf.n3_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n3_3_fold))
### Test set diff
diff_drybean_tda_pca_5.60.5_rf.n3_test<-(db_rf_cf_ov_acc-db_tda_pc_5.60.5_n3_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_rf.n3_test
## Accuracy
## 0.4867647
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n3_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n3_test$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n3_test$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_rf.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_rf.n3_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1588333
##
## $winRight
## [1] 0.8411667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_rf.n3_test))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf.n3_test)) #bf_tda_pca_5.60.5_rf.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n2_test)
##Node4
DryBean_TDA_PC_5.60.5_n4_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n4.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_PC_5.60.5_n4_RfFit0
## Random Forest
##
## 894 samples
## 16 predictor
## 4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 596, 596, 596
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9731544 0.9565649
## 100 0.9731544 0.9565649
## 150 0.9731544 0.9565649
## 200 0.9731544 0.9565649
## 250 0.9731544 0.9565649
## 300 0.9731544 0.9565649
## 350 0.9731544 0.9565649
## 400 0.9731544 0.9565649
## 450 0.9731544 0.9565649
## 500 0.9731544 0.9565649
## 550 0.9731544 0.9565649
## 600 0.9731544 0.9565649
## 650 0.9731544 0.9565649
## 700 0.9731544 0.9565649
## 750 0.9720358 0.9547399
## 800 0.9731544 0.9565649
## 850 0.9731544 0.9565649
## 900 0.9731544 0.9565649
## 950 0.9731544 0.9565649
## 1000 0.9742729 0.9583984
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 1000.
DryBean_TDA_PC_5.60.5_n4_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.9765101 0.9616854 Fold2
## 2 0.9630872 0.9406299 Fold1
## 3 0.9832215 0.9728800 Fold3
db_tda_pc_5.60.5_n4_rf_fit0_re<-DryBean_TDA_PC_5.60.5_n4_RfFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n4_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 894 factor numeric
## err.rate 2500 -none- numeric
## confusion 20 -none- numeric
## votes 3576 matrix numeric
## oob.times 894 -none- numeric
## classes 4 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 894 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 4 -none- character
## param 1 -none- list
vip(DryBean_TDA_PC_5.60.5_n4_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n4_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_PC_5.60.5_n4_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n4_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n4_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n4_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 92 1 0 0 0 1 0
## BOMBAY 0 155 0 0 0 0 0
## CALI 259 0 456 0 5 71 2
## DERMASON 0 0 0 0 0 0 0
## HOROZ 45 0 33 1063 573 536 788
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3127
## 95% CI : (0.2985, 0.3272)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 5.244e-14
##
## Kappa : 0.2078
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.23232 0.99359 0.9325 0.0000
## Specificity 0.99946 1.00000 0.9062 1.0000
## Pos Pred Value 0.97872 1.00000 0.5750 NaN
## Neg Pred Value 0.92373 0.99975 0.9900 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.02255 0.03799 0.1118 0.0000
## Detection Prevalence 0.02304 0.03799 0.1944 0.0000
## Balanced Accuracy 0.61589 0.99679 0.9193 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9913 0.000 0.0000
## Specificity 0.2961 1.000 1.0000
## Pos Pred Value 0.1886 NaN NaN
## Neg Pred Value 0.9952 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1404 0.000 0.0000
## Detection Prevalence 0.7446 0.000 0.0000
## Balanced Accuracy 0.6437 0.500 0.5000
db_tda_pc_5.60.5_n4_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 92 1 0 0 0 1 0
## BOMBAY 0 155 0 0 0 0 0
## CALI 259 0 456 0 5 71 2
## DERMASON 0 0 0 0 0 0 0
## HOROZ 45 0 33 1063 573 536 788
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3127
## 95% CI : (0.2985, 0.3272)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 5.244e-14
##
## Kappa : 0.2078
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.23232 0.99359 0.9325 0.0000
## Specificity 0.99946 1.00000 0.9062 1.0000
## Pos Pred Value 0.97872 1.00000 0.5750 NaN
## Neg Pred Value 0.92373 0.99975 0.9900 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.02255 0.03799 0.1118 0.0000
## Detection Prevalence 0.02304 0.03799 0.1944 0.0000
## Balanced Accuracy 0.61589 0.99679 0.9193 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9913 0.000 0.0000
## Specificity 0.2961 1.000 1.0000
## Pos Pred Value 0.1886 NaN NaN
## Neg Pred Value 0.9952 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1404 0.000 0.0000
## Detection Prevalence 0.7446 0.000 0.0000
## Balanced Accuracy 0.6437 0.500 0.5000
db_tda_pc_5.60.5_n4_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.127451e-01 2.078029e-01 2.985351e-01 3.272237e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 5.244457e-14 NaN
db_tda_pc_5.60.5_n4_rf_cf0_ov_acc<-db_tda_pc_5.60.5_n4_rf_cf0$overall[1]
db_tda_pc_5.60.5_n4_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.2323232 0.9994571 0.9787234 0.9237331 0.9787234
## Class: BOMBAY 0.9935897 1.0000000 1.0000000 0.9997452 1.0000000
## Class: CALI 0.9325153 0.9061543 0.5750315 0.9899605 0.5750315
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.9913495 0.2961165 0.1886109 0.9952015 0.1886109
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.2323232 0.3755102 0.09705882 0.02254902
## Class: BOMBAY 0.9935897 0.9967846 0.03823529 0.03799020
## Class: CALI 0.9325153 0.7113885 0.11985294 0.11176471
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.9913495 0.3169248 0.14166667 0.14044118
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.02303922 0.6158902
## Class: BOMBAY 0.03799020 0.9967949
## Class: CALI 0.19436275 0.9193348
## Class: DERMASON 0.00000000 0.5000000
## Class: HOROZ 0.74460784 0.6437330
## Class: SEKER 0.00000000 0.5000000
## Class: SIRA 0.00000000 0.5000000
db_tda_pc_5.60.5_n4_rf_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n4_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_rf_n4_3_fold<-(db_rf_fit_re-db_tda_pc_5.60.5_n4_rf_fit0_re)
diff_drybean_tda_pca_5.60.5_rf_n4_3_fold
## Accuracy
## 1 -0.05425637
## 2 -0.03737116
## 3 -0.06978112
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_rf.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_rf.n4_3_fold
## $winLeft
## [1] 0.9908
##
## $winRope
## [1] 0.0092
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n4_3_fold
## $left
## [1] 0.9720913
##
## $rope
## [1] 0.01415416
##
## $right
## [1] 0.01375451
# Rope Plot
plot(rope(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold),c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold))
#bf_tda_pca_5.60.5_rf.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_rf_n4_3_fold)
## t = -5.749, df = 2, p-value = 0.02895
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.09407011 -0.01353566
## sample estimates:
## mean of x
## -0.05380289
### Test set diff
diff_drybean_tda_pca_5.60.5_rf.n4_test<-(db_rf_cf_ov_acc-db_tda_pc_5.60.5_n4_rf_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_rf.n4_test
## Accuracy
## 0.6112745
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_rf.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_rf.n4_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n4_test$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n4_test$probRight
bst_dbf_db_tda_pca_5.60.5_rf.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_rf.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_rf.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1589333
##
## $winRight
## [1] 0.8410667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_rf.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_rf.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_rf.n4_test))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf.n4_test)) #bf_tda_pca_5.60.5_rf.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n4_test))
##Node5
#DryBean_TDA_PC_5.60.5_n5_RfFit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.60.5.n5.vec,
# Importance = T,
# method = 'rf',
# trControl = fitControl,
# tuneGrid = rfGrid, preProc = c('center','scale'),
# metric='Accuracy')
#DryBean_TDA_PC_5.60.5_n5_RfFit0
#DryBean_TDA_PC_5.60.5_n5_RfFit0$resample
#db_tda_pc_5.60.5_n5_rf_fit0_re<-DryBean_TDA_PC_5.60.5_n5_RfFit0$resample[1]
#summary(DryBean_TDA_PC_5.60.5_n5_RfFit0)
#vip(DryBean_TDA_PC_5.60.5_n5_RfFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n5_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_PC_5.60.5_n5_RfFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.60.5_n5_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.60.5_n5_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.60.5_n5_rf_cf0
#db_tda_pc_5.60.5_n5_rf_cf0
#db_tda_pc_5.60.5_n5_rf_cf0$overall
#db_tda_pc_5.60.5_n5_rf_cf0_ov_acc<-db_tda_pc_5.60.5_n5_rf_cf0$overall[1]
#db_tda_pc_5.60.5_n5_rf_cf0$byClass
#db_tda_pc_5.60.5_n5_rf_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n5_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_pca_5.60.5_rf_n5_3_fold<-(db_rf_fit_re-db_tda_pc_5.60.5_n5_rf_fit0_re)
#diff_drybean_tda_pca_5.60.5_rf_n5_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_rf.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_rf.n5_3_fold
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_rf.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.60.5_rf.n5_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_rf.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_rf.n5_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_rf.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_rf.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_rf_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf_n5_3_fold))
#bf_tda_pca_5.60.5_rf.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf_n5_3_fold))
### Test set diff
#diff_drybean_tda_pca_5.60.5_rf.n5_test<-(db_rf_cf_ov_acc-db_tda_pc_5.60.5_n5_rf_cf0_ov_acc)
#diff_drybean_tda_pca_5.60.5_rf.n5_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_rf.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_rf.n5_test
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_rf.n5_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_rf.n5_test$probLeft/bst_dbf_db_tda_pca_5.60.5_rf.n5_test$probRight
#bst_dbf_db_tda_pca_5.60.5_rf.n5_test_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_rf.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_rf.n5_test
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_rf.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_rf.n5_test
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_rf.n5_test))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_rf.n5_test)) #bf_tda_pca_5.60.5_rf.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_rf.n5_test))
##With TDA KDE filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_KDE_5.60.5_n1_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n1.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.60.5_n1_RfFit0
## Random Forest
##
## 7503 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5002, 5002, 5002
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9462882 0.9352868
## 100 0.9468213 0.9359354
## 150 0.9473544 0.9365736
## 200 0.9466880 0.9357703
## 250 0.9461549 0.9351244
## 300 0.9470878 0.9362528
## 350 0.9457550 0.9346471
## 400 0.9460216 0.9349740
## 450 0.9470878 0.9362530
## 500 0.9461549 0.9351235
## 550 0.9458883 0.9348097
## 600 0.9468213 0.9359354
## 650 0.9456218 0.9344775
## 700 0.9458883 0.9348037
## 750 0.9470878 0.9362523
## 800 0.9462882 0.9352864
## 850 0.9474877 0.9367392
## 900 0.9469546 0.9360866
## 950 0.9466880 0.9357708
## 1000 0.9460216 0.9349682
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 850.
DryBean_TDA_KDE_5.60.5_n1_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.9436226 0.9320860 Fold3
## 2 0.9500200 0.9398229 Fold2
## 3 0.9488205 0.9383085 Fold1
ad_tda_kde_5.60.5_n1_rf_fit0_re<-DryBean_TDA_KDE_5.60.5_n1_RfFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n1_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 7503 factor numeric
## err.rate 4000 -none- numeric
## confusion 56 -none- numeric
## votes 52521 matrix numeric
## oob.times 7503 -none- numeric
## classes 7 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 7503 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 7 -none- character
## param 1 -none- list
vip(DryBean_TDA_KDE_5.60.5_n1_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n1_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n1_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n1_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n1_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
ad_tda_kde_5.60.5_n1_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 391 0 0 1 0 0 3
## BOMBAY 0 156 0 0 0 0 0
## CALI 0 0 486 0 0 0 0
## DERMASON 1 0 0 801 4 3 45
## HOROZ 0 0 1 3 572 0 2
## SEKER 0 0 0 36 0 592 11
## SIRA 4 0 2 222 2 13 729
##
## Overall Statistics
##
## Accuracy : 0.9135
## 95% CI : (0.9044, 0.9219)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8959
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98737 1.00000 0.9939 0.7535
## Specificity 0.99891 1.00000 1.0000 0.9824
## Pos Pred Value 0.98987 1.00000 1.0000 0.9379
## Neg Pred Value 0.99864 1.00000 0.9992 0.9188
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.09583 0.03824 0.1191 0.1963
## Detection Prevalence 0.09681 0.03824 0.1191 0.2093
## Balanced Accuracy 0.99314 1.00000 0.9969 0.8680
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9896 0.9737 0.9228
## Specificity 0.9983 0.9865 0.9261
## Pos Pred Value 0.9896 0.9264 0.7500
## Neg Pred Value 0.9983 0.9954 0.9804
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1402 0.1451 0.1787
## Detection Prevalence 0.1417 0.1566 0.2382
## Balanced Accuracy 0.9940 0.9801 0.9245
ad_tda_kde_5.60.5_n1_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 391 0 0 1 0 0 3
## BOMBAY 0 156 0 0 0 0 0
## CALI 0 0 486 0 0 0 0
## DERMASON 1 0 0 801 4 3 45
## HOROZ 0 0 1 3 572 0 2
## SEKER 0 0 0 36 0 592 11
## SIRA 4 0 2 222 2 13 729
##
## Overall Statistics
##
## Accuracy : 0.9135
## 95% CI : (0.9044, 0.9219)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8959
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98737 1.00000 0.9939 0.7535
## Specificity 0.99891 1.00000 1.0000 0.9824
## Pos Pred Value 0.98987 1.00000 1.0000 0.9379
## Neg Pred Value 0.99864 1.00000 0.9992 0.9188
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.09583 0.03824 0.1191 0.1963
## Detection Prevalence 0.09681 0.03824 0.1191 0.2093
## Balanced Accuracy 0.99314 1.00000 0.9969 0.8680
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9896 0.9737 0.9228
## Specificity 0.9983 0.9865 0.9261
## Pos Pred Value 0.9896 0.9264 0.7500
## Neg Pred Value 0.9983 0.9954 0.9804
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1402 0.1451 0.1787
## Detection Prevalence 0.1417 0.1566 0.2382
## Balanced Accuracy 0.9940 0.9801 0.9245
ad_tda_kde_5.60.5_n1_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9134804 0.8958590 0.9044325 0.9219309 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
ad_tda_kde_5.60.5_n1_rf_cf0_ov_acc<-ad_tda_kde_5.60.5_n1_rf_cf0$overall[1]
ad_tda_kde_5.60.5_n1_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.9873737 0.9989142 0.9898734 0.9986431 0.9898734
## Class: BOMBAY 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## Class: CALI 0.9938650 1.0000000 1.0000000 0.9991653 1.0000000
## Class: DERMASON 0.7535278 0.9824329 0.9379391 0.9187849 0.9379391
## Class: HOROZ 0.9896194 0.9982867 0.9896194 0.9982867 0.9896194
## Class: SEKER 0.9736842 0.9864631 0.9264476 0.9953502 0.9264476
## Class: SIRA 0.9227848 0.9261398 0.7500000 0.9803732 0.7500000
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9873737 0.9886220 0.09705882 0.09583333
## Class: BOMBAY 1.0000000 1.0000000 0.03823529 0.03823529
## Class: CALI 0.9938650 0.9969231 0.11985294 0.11911765
## Class: DERMASON 0.7535278 0.8356808 0.26053922 0.19632353
## Class: HOROZ 0.9896194 0.9896194 0.14166667 0.14019608
## Class: SEKER 0.9736842 0.9494787 0.14901961 0.14509804
## Class: SIRA 0.9227848 0.8274688 0.19362745 0.17867647
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09681373 0.9931440
## Class: BOMBAY 0.03823529 1.0000000
## Class: CALI 0.11911765 0.9969325
## Class: DERMASON 0.20931373 0.8679803
## Class: HOROZ 0.14166667 0.9939530
## Class: SEKER 0.15661765 0.9800737
## Class: SIRA 0.23823529 0.9244623
ad_tda_kde_5.60.5_n1_rf_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n1_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_rf_n1_3_fold<-(db_rf_fit_re-ad_tda_kde_5.60.5_n1_rf_fit0_re)
diff_drybean_tda_kde_5.60.5_rf_n1_3_fold
## Accuracy
## 1 -0.02136885
## 2 -0.02430391
## 3 -0.03538012
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n1_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n1_3_fold_odds.left<-bst_tda_kde_5.60.5_rf.n1_3_fold$probLeft/bst_tda_kde_5.60.5_rf.n1_3_fold$probRight
bst_tda_kde_5.60.5_rf.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n1_3_fold
## $winLeft
## [1] 0.9912667
##
## $winRope
## [1] 0.008733333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n1_3_fold
## $left
## [1] 0.9627262
##
## $rope
## [1] 0.02864751
##
## $right
## [1] 0.00862633
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold))
#bf_tda_kde_5.60.5_rf.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_rf_n1_3_fold)
## t = -6.3329, df = 2, p-value = 0.02404
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.045373724 -0.008661529
## sample estimates:
## mean of x
## -0.02701763
### Test set diff
diff_drybean_tda_kde_5.60.5_rf.n1_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.60.5_n1_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_rf.n1_test
## Accuracy
## 0.01053922
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n1_test),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n1_test_odds.left<-bst_tda_kde_5.60.5_rf.n1_test$probLeft/bst_tda_kde_5.60.5_rf.n1_test$probRight
bst_tda_kde_5.60.5_rf.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n1_test),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.4577
##
## $winRight
## [1] 0.5423
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf.n1_test))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf.n1_test)) #bf_tda_kde_5.60.5_rf.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n1_test))
##Node2
DryBean_TDA_KDE_5.60.5_n2_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n2.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.60.5_n2_RfFit0
## Random Forest
##
## 7002 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4667, 4669, 4668
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9444442 0.9285797
## 100 0.9450154 0.9293120
## 150 0.9444438 0.9285704
## 200 0.9455864 0.9300511
## 250 0.9447294 0.9289374
## 300 0.9444440 0.9285760
## 350 0.9460147 0.9305963
## 400 0.9444441 0.9285848
## 450 0.9445866 0.9287581
## 500 0.9453010 0.9296870
## 550 0.9458722 0.9304104
## 600 0.9453009 0.9296828
## 650 0.9453010 0.9296819
## 700 0.9457292 0.9302281
## 750 0.9457297 0.9302372
## 800 0.9454438 0.9298669
## 850 0.9455865 0.9300402
## 900 0.9460147 0.9305929
## 950 0.9457292 0.9302317
## 1000 0.9451583 0.9295105
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 350.
DryBean_TDA_KDE_5.60.5_n2_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.9468495 0.9315144 Fold2
## 2 0.9516060 0.9378969 Fold1
## 3 0.9395887 0.9223775 Fold3
ad_tda_KDE_5.60.5_n2_rf_fit0_re<-DryBean_TDA_KDE_5.60.5_n2_RfFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n2_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 7002 factor numeric
## err.rate 3500 -none- numeric
## confusion 42 -none- numeric
## votes 42012 matrix numeric
## oob.times 7002 -none- numeric
## classes 6 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 7002 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 6 -none- character
## param 1 -none- list
vip(DryBean_TDA_KDE_5.60.5_n2_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n2_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n2_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n2_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n2_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n2_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 330 12 17 0 10 1 2
## BOMBAY 0 0 0 0 0 0 0
## CALI 20 142 462 0 14 0 1
## DERMASON 1 0 0 1029 3 16 96
## HOROZ 1 0 3 1 551 0 2
## SEKER 36 2 1 8 0 586 7
## SIRA 8 0 6 25 0 5 682
##
## Overall Statistics
##
## Accuracy : 0.8922
## 95% CI : (0.8822, 0.9015)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8688
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.83333 0.00000 0.9448 0.9680
## Specificity 0.98860 1.00000 0.9507 0.9616
## Pos Pred Value 0.88710 NaN 0.7230 0.8987
## Neg Pred Value 0.98220 0.96176 0.9922 0.9884
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08088 0.00000 0.1132 0.2522
## Detection Prevalence 0.09118 0.00000 0.1566 0.2806
## Balanced Accuracy 0.91097 0.50000 0.9477 0.9648
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9533 0.9638 0.8633
## Specificity 0.9980 0.9844 0.9866
## Pos Pred Value 0.9875 0.9156 0.9394
## Neg Pred Value 0.9923 0.9936 0.9678
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1350 0.1436 0.1672
## Detection Prevalence 0.1368 0.1569 0.1779
## Balanced Accuracy 0.9756 0.9741 0.9250
ad_tda_kde_5.60.5_n2_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 330 12 17 0 10 1 2
## BOMBAY 0 0 0 0 0 0 0
## CALI 20 142 462 0 14 0 1
## DERMASON 1 0 0 1029 3 16 96
## HOROZ 1 0 3 1 551 0 2
## SEKER 36 2 1 8 0 586 7
## SIRA 8 0 6 25 0 5 682
##
## Overall Statistics
##
## Accuracy : 0.8922
## 95% CI : (0.8822, 0.9015)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8688
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.83333 0.00000 0.9448 0.9680
## Specificity 0.98860 1.00000 0.9507 0.9616
## Pos Pred Value 0.88710 NaN 0.7230 0.8987
## Neg Pred Value 0.98220 0.96176 0.9922 0.9884
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08088 0.00000 0.1132 0.2522
## Detection Prevalence 0.09118 0.00000 0.1566 0.2806
## Balanced Accuracy 0.91097 0.50000 0.9477 0.9648
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9533 0.9638 0.8633
## Specificity 0.9980 0.9844 0.9866
## Pos Pred Value 0.9875 0.9156 0.9394
## Neg Pred Value 0.9923 0.9936 0.9678
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1350 0.1436 0.1672
## Detection Prevalence 0.1368 0.1569 0.1779
## Balanced Accuracy 0.9756 0.9741 0.9250
ad_tda_kde_5.60.5_n2_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8921569 0.8688131 0.8822342 0.9015139 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
ad_tda_kde_5.60.5_n2_rf_cf0_ov_acc<-ad_tda_kde_5.60.5_n2_rf_cf0$overall[1]
ad_tda_kde_5.60.5_n2_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8333333 0.9885993 0.8870968 0.9822006 0.8870968
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9447853 0.9507101 0.7230047 0.9921534 0.7230047
## Class: DERMASON 0.9680151 0.9615512 0.8986900 0.9884157 0.8986900
## Class: HOROZ 0.9532872 0.9980011 0.9874552 0.9923339 0.9874552
## Class: SEKER 0.9638158 0.9844470 0.9156250 0.9936047 0.9156250
## Class: SIRA 0.8632911 0.9866261 0.9393939 0.9677996 0.9393939
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8333333 0.8593750 0.09705882 0.08088235
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.9447853 0.8191489 0.11985294 0.11323529
## Class: DERMASON 0.9680151 0.9320652 0.26053922 0.25220588
## Class: HOROZ 0.9532872 0.9700704 0.14166667 0.13504902
## Class: SEKER 0.9638158 0.9391026 0.14901961 0.14362745
## Class: SIRA 0.8632911 0.8997361 0.19362745 0.16715686
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09117647 0.9109663
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.15661765 0.9477477
## Class: DERMASON 0.28063725 0.9647831
## Class: HOROZ 0.13676471 0.9756442
## Class: SEKER 0.15686275 0.9741314
## Class: SIRA 0.17794118 0.9249586
ad_tda_kde_5.60.5_n2_rf_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n2_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_rf_n2_3_fold<-(db_rf_fit_re-ad_tda_KDE_5.60.5_n2_rf_fit0_re)
diff_drybean_tda_kde_5.60.5_rf_n2_3_fold
## Accuracy
## 1 -0.02459585
## 2 -0.02588991
## 3 -0.02614834
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n2_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n2_3_fold_odds.left<-bst_tda_kde_5.60.5_rf.n2_3_fold$probLeft/bst_tda_kde_5.60.5_rf.n2_3_fold$probRight
bst_tda_kde_5.60.5_rf.n2_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n2_3_fold
## $winLeft
## [1] 0.9916333
##
## $winRope
## [1] 0.008366667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n2_3_fold
## $left
## [1] 0.9993649
##
## $rope
## [1] 0.0005134639
##
## $right
## [1] 0.0001216583
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold))
#bf_tda_kde_5.60.5_rf.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_rf_n2_3_fold)
## t = -53.19, df = 2, p-value = 0.0003533
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.02761106 -0.02347834
## sample estimates:
## mean of x
## -0.0255447
### Test set diff
diff_drybean_tda_kde_5.60.5_rf.n2_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.60.5_n2_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_rf.n2_test
## Accuracy
## 0.03186275
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n2_test),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n2_test_odds.left<-bst_tda_kde_5.60.5_rf.n2_test$probLeft/bst_tda_kde_5.60.5_rf.n2_test$probRight
bst_tda_kde_5.60.5_rf.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n2_test),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1591
##
## $winRight
## [1] 0.8409
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf.n2_test))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf.n2_test)) #bf_tda_kde_5.60.5_rf.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n2_test))
##Node3
DryBean_TDA_KDE_5.60.5_n3_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n3.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 50 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 100 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 150 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 200 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 250 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 300 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 350 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 400 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 450 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 500 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 550 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 600 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 650 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 700 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 750 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 800 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 850 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 900 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry= 950 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold2: mtry=1000 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.60.5_n3_RfFit0
## Random Forest
##
## 3511 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 2342, 2339, 2341
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.9097929 0.8633737
## 100 0.9085108 0.8613713
## 150 0.9085101 0.8613397
## 200 0.9089385 0.8620215
## 250 0.9089378 0.8619806
## 300 0.9080827 0.8606882
## 350 0.9080838 0.8607785
## 400 0.9072280 0.8594751
## 450 0.9080827 0.8607326
## 500 0.9046628 0.8556050
## 550 0.9080835 0.8607980
## 600 0.9059456 0.8574932
## 650 0.9093655 0.8626489
## 700 0.9093666 0.8626688
## 750 0.9097932 0.8632842
## 800 0.9072273 0.8594473
## 850 0.9076561 0.8600941
## 900 0.9068003 0.8587768
## 950 0.9063722 0.8581167
## 1000 0.9093662 0.8626360
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 750.
DryBean_TDA_KDE_5.60.5_n3_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.9161677 0.8730770 Fold1
## 2 0.9034188 0.8534913 Fold3
## 3 NA NA Fold2
ad_tda_kde_5.60.5_n3_rf_fit0_re<-DryBean_TDA_KDE_5.60.5_n3_RfFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n3_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 3511 factor numeric
## err.rate 3500 -none- numeric
## confusion 42 -none- numeric
## votes 21066 matrix numeric
## oob.times 3511 -none- numeric
## classes 6 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3511 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 6 -none- character
## param 1 -none- list
vip(DryBean_TDA_KDE_5.60.5_n3_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n3_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n3_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n3_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n3_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n3_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 45 9 4 0 2 3 5
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 1 0 0 0 0
## DERMASON 0 0 0 1051 8 14 69
## HOROZ 233 8 131 1 509 0 3
## SEKER 17 3 1 3 0 577 8
## SIRA 101 136 352 8 59 14 705
##
## Overall Statistics
##
## Accuracy : 0.7078
## 95% CI : (0.6936, 0.7218)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6381
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.11364 0.00000 0.0020450 0.9887
## Specificity 0.99376 1.00000 1.0000000 0.9698
## Pos Pred Value 0.66176 NaN 1.0000000 0.9203
## Neg Pred Value 0.91251 0.96176 0.8803628 0.9959
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.01103 0.00000 0.0002451 0.2576
## Detection Prevalence 0.01667 0.00000 0.0002451 0.2799
## Balanced Accuracy 0.55370 0.50000 0.5010225 0.9793
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8806 0.9490 0.8924
## Specificity 0.8926 0.9908 0.7964
## Pos Pred Value 0.5751 0.9475 0.5127
## Neg Pred Value 0.9784 0.9911 0.9686
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1248 0.1414 0.1728
## Detection Prevalence 0.2169 0.1493 0.3370
## Balanced Accuracy 0.8866 0.9699 0.8444
ad_tda_kde_5.60.5_n3_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 45 9 4 0 2 3 5
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 1 0 0 0 0
## DERMASON 0 0 0 1051 8 14 69
## HOROZ 233 8 131 1 509 0 3
## SEKER 17 3 1 3 0 577 8
## SIRA 101 136 352 8 59 14 705
##
## Overall Statistics
##
## Accuracy : 0.7078
## 95% CI : (0.6936, 0.7218)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.6381
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.11364 0.00000 0.0020450 0.9887
## Specificity 0.99376 1.00000 1.0000000 0.9698
## Pos Pred Value 0.66176 NaN 1.0000000 0.9203
## Neg Pred Value 0.91251 0.96176 0.8803628 0.9959
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.01103 0.00000 0.0002451 0.2576
## Detection Prevalence 0.01667 0.00000 0.0002451 0.2799
## Balanced Accuracy 0.55370 0.50000 0.5010225 0.9793
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8806 0.9490 0.8924
## Specificity 0.8926 0.9908 0.7964
## Pos Pred Value 0.5751 0.9475 0.5127
## Neg Pred Value 0.9784 0.9911 0.9686
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1248 0.1414 0.1728
## Detection Prevalence 0.2169 0.1493 0.3370
## Balanced Accuracy 0.8866 0.9699 0.8444
ad_tda_kde_5.60.5_n3_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.7078431 0.6380617 0.6936214 0.7217667 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
ad_tda_kde_5.60.5_n3_rf_cf0_ov_acc<-ad_tda_kde_5.60.5_n3_rf_cf0$overall[1]
ad_tda_kde_5.60.5_n3_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.11363636 0.9937568 0.6617647 0.9125125 0.6617647
## Class: BOMBAY 0.00000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.00204499 1.0000000 1.0000000 0.8803628 1.0000000
## Class: DERMASON 0.98871119 0.9698376 0.9203152 0.9959156 0.9203152
## Class: HOROZ 0.88062284 0.8926328 0.5751412 0.9784038 0.5751412
## Class: SEKER 0.94901316 0.9907834 0.9474548 0.9910689 0.9474548
## Class: SIRA 0.89240506 0.7963526 0.5127273 0.9685767 0.5127273
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.11363636 0.193965517 0.09705882 0.011029412
## Class: BOMBAY 0.00000000 NA 0.03823529 0.000000000
## Class: CALI 0.00204499 0.004081633 0.11985294 0.000245098
## Class: DERMASON 0.98871119 0.953287982 0.26053922 0.257598039
## Class: HOROZ 0.88062284 0.695830485 0.14166667 0.124754902
## Class: SEKER 0.94901316 0.948233361 0.14901961 0.141421569
## Class: SIRA 0.89240506 0.651270208 0.19362745 0.172794118
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.016666667 0.5536966
## Class: BOMBAY 0.000000000 0.5000000
## Class: CALI 0.000245098 0.5010225
## Class: DERMASON 0.279901961 0.9792744
## Class: HOROZ 0.216911765 0.8866278
## Class: SEKER 0.149264706 0.9698983
## Class: SIRA 0.337009804 0.8443788
ad_tda_kde_5.60.5_n3_rf_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n3_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_rf_n3_3_fold<-(db_rf_fit_re-ad_tda_kde_5.60.5_n3_rf_fit0_re)
diff_drybean_tda_kde_5.60.5_rf_n3_3_fold
## Accuracy
## 1 0.006086034
## 2 0.022297281
## 3 NA
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n3_3_fold
## $probLeft
## [1] NA
##
## $probRope
## [1] NA
##
## $probRight
## [1] NA
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n3_3_fold_odds.left<-bst_tda_kde_5.60.5_rf.n3_3_fold$probLeft/bst_tda_kde_5.60.5_rf.n3_3_fold$probRight
bst_tda_kde_5.60.5_rf.n3_3_fold_odds.left
## [1] NA
# Bayesian Signed Rank Test
#bsr_tda_kde_5.60.5_rf.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n3_3_fold),-0.01,0.01)
#bsr_tda_kde_5.60.5_rf.n3_3_fold
# Bayesian Correlated Test
#bct_tda_kde_5.60.5_rf.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n3_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.60.5_rf.n3_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf_n3_3_fold))
#bf_tda_kde_5.60.5_rf.n3_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n3_3_fold))
### Test set diff
diff_drybean_tda_kde_5.60.5_rf.n3_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.60.5_n3_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_rf.n3_test
## Accuracy
## 0.2161765
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n3_test),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n3_test_odds.left<-bst_tda_kde_5.60.5_rf.n3_test$probLeft/bst_tda_kde_5.60.5_rf.n3_test$probRight
bst_tda_kde_5.60.5_rf.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n3_test),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n3_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1599
##
## $winRight
## [1] 0.8401
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf.n3_test))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf.n3_test)) #bf_tda_kde_5.60.5_rf.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n3_test))
##Node4
DryBean_TDA_KDE_5.60.5_n4_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n4.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.60.5_n4_RfFit0
## Random Forest
##
## 1759 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 1173, 1172, 1173
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.8055790 0.6719209
## 100 0.8044394 0.6700029
## 150 0.8078523 0.6759512
## 200 0.8078523 0.6753621
## 250 0.8072855 0.6748779
## 300 0.8089890 0.6772184
## 350 0.8061459 0.6728635
## 400 0.8067157 0.6742178
## 450 0.8106965 0.6800007
## 500 0.8095569 0.6782018
## 550 0.8095579 0.6784758
## 600 0.8067166 0.6737180
## 650 0.8101257 0.6791846
## 700 0.8112653 0.6808640
## 750 0.8112643 0.6808839
## 800 0.8084212 0.6764512
## 850 0.8078543 0.6760808
## 900 0.8078494 0.6758194
## 950 0.8084251 0.6764376
## 1000 0.8089910 0.6773813
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 700.
DryBean_TDA_KDE_5.60.5_n4_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.8259386 0.7044911 Fold1
## 2 0.8122867 0.6856285 Fold3
## 3 0.7955707 0.6524725 Fold2
ad_tda_kde_5.60.5_n4_rf_fit0_re<-DryBean_TDA_KDE_5.60.5_n4_RfFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n4_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 1759 factor numeric
## err.rate 2500 -none- numeric
## confusion 20 -none- numeric
## votes 7036 matrix numeric
## oob.times 1759 -none- numeric
## classes 4 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 1759 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 4 -none- character
## param 1 -none- list
vip(DryBean_TDA_KDE_5.60.5_n4_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n4_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n4_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n4_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n4_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n4_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 352 145 482 999 539 21 329
## HOROZ 0 0 0 0 1 0 0
## SEKER 14 3 1 15 0 573 6
## SIRA 30 8 6 49 38 14 455
##
## Overall Statistics
##
## Accuracy : 0.4971
## 95% CI : (0.4816, 0.5125)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3435
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9398
## Specificity 1.00000 1.00000 1.0000 0.3808
## Pos Pred Value NaN NaN NaN 0.3484
## Neg Pred Value 0.90294 0.96176 0.8801 0.9472
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2449
## Detection Prevalence 0.00000 0.00000 0.0000 0.7027
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6603
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0017301 0.9424 0.5759
## Specificity 1.0000000 0.9888 0.9559
## Pos Pred Value 1.0000000 0.9363 0.7583
## Neg Pred Value 0.8585438 0.9899 0.9037
## Prevalence 0.1416667 0.1490 0.1936
## Detection Rate 0.0002451 0.1404 0.1115
## Detection Prevalence 0.0002451 0.1500 0.1471
## Balanced Accuracy 0.5008651 0.9656 0.7659
ad_tda_kde_5.60.5_n4_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 352 145 482 999 539 21 329
## HOROZ 0 0 0 0 1 0 0
## SEKER 14 3 1 15 0 573 6
## SIRA 30 8 6 49 38 14 455
##
## Overall Statistics
##
## Accuracy : 0.4971
## 95% CI : (0.4816, 0.5125)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3435
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9398
## Specificity 1.00000 1.00000 1.0000 0.3808
## Pos Pred Value NaN NaN NaN 0.3484
## Neg Pred Value 0.90294 0.96176 0.8801 0.9472
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2449
## Detection Prevalence 0.00000 0.00000 0.0000 0.7027
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6603
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0017301 0.9424 0.5759
## Specificity 1.0000000 0.9888 0.9559
## Pos Pred Value 1.0000000 0.9363 0.7583
## Neg Pred Value 0.8585438 0.9899 0.9037
## Prevalence 0.1416667 0.1490 0.1936
## Detection Rate 0.0002451 0.1404 0.1115
## Detection Prevalence 0.0002451 0.1500 0.1471
## Balanced Accuracy 0.5008651 0.9656 0.7659
ad_tda_kde_5.60.5_n4_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.970588e-01 3.434684e-01 4.816011e-01 5.125208e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 7.312327e-228 NaN
ad_tda_kde_5.60.5_n4_rf_cf0_ov_acc<-ad_tda_kde_5.60.5_n4_rf_cf0$overall[1]
ad_tda_kde_5.60.5_n4_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.000000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.000000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.000000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.939793039 0.3808419 0.3484479 0.9472383 0.3484479
## Class: HOROZ 0.001730104 1.0000000 1.0000000 0.8585438 1.0000000
## Class: SEKER 0.942434211 0.9887673 0.9362745 0.9899077 0.9362745
## Class: SIRA 0.575949367 0.9559271 0.7583333 0.9037356 0.7583333
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.000000000 NA 0.09705882 0.000000000
## Class: BOMBAY 0.000000000 NA 0.03823529 0.000000000
## Class: CALI 0.000000000 NA 0.11985294 0.000000000
## Class: DERMASON 0.939793039 0.508396947 0.26053922 0.244852941
## Class: HOROZ 0.001730104 0.003454231 0.14166667 0.000245098
## Class: SEKER 0.942434211 0.939344262 0.14901961 0.140441176
## Class: SIRA 0.575949367 0.654676259 0.19362745 0.111519608
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.000000000 0.5000000
## Class: BOMBAY 0.000000000 0.5000000
## Class: CALI 0.000000000 0.5000000
## Class: DERMASON 0.702696078 0.6603175
## Class: HOROZ 0.000245098 0.5008651
## Class: SEKER 0.150000000 0.9656007
## Class: SIRA 0.147058824 0.7659382
ad_tda_kde_5.60.5_n4_rf_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n4_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_rf_n4_3_fold<-(db_rf_fit_re-ad_tda_kde_5.60.5_n4_rf_fit0_re)
diff_drybean_tda_kde_5.60.5_rf_n4_3_fold
## Accuracy
## 1 0.09631513
## 2 0.11342939
## 3 0.11786965
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n4_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n4_3_fold_odds.left<-bst_tda_kde_5.60.5_rf.n4_3_fold$probLeft/bst_tda_kde_5.60.5_rf.n4_3_fold$probRight
bst_tda_kde_5.60.5_rf.n4_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n4_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.009033333
##
## $winRight
## [1] 0.9909667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n4_3_fold
## $left
## [1] 0.002013533
##
## $rope
## [1] 0.0008859466
##
## $right
## [1] 0.9971005
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold,c(-0.01,0.01)))
### Test set diff
diff_drybean_tda_kde_5.60.5_rf.n4_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.60.5_n4_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_rf.n4_test
## Accuracy
## 0.4269608
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n4_test),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
#BayesFactor
#bf_tda_kde_5.60.5_rf.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold))
#bf_tda_kde_5.60.5_rf.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_rf_n4_3_fold)
## t = 16.619, df = 2, p-value = 0.003601
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.08093188 0.13747758
## sample estimates:
## mean of x
## 0.1092047
bst_tda_kde_5.60.5_rf.n4_test_odds.left<-bst_tda_kde_5.60.5_rf.n4_test$probLeft/bst_tda_kde_5.60.5_rf.n4_test$probRight
bst_tda_kde_5.60.5_rf.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n4_test),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1593
##
## $winRight
## [1] 0.8407
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf.n4_test))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf.n4_test)) #bf_tda_kde_5.60.5_rf.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n4_test))
##Node5
DryBean_TDA_KDE_5.60.5_n5_RfFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n5.vec,
Importance = T,
method = 'rf',
trControl = fitControl,
tuneGrid = rfGrid, preProc = c('center','scale'),
metric='Accuracy')
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 50 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 100 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 150 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 200 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 250 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 300 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 350 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 400 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 450 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 500 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 550 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 600 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 650 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 700 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 750 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 800 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 850 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 900 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry= 950 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
## Warning: model fit failed for Fold3: mtry=1000 Error in randomForest.default(x, y, mtry = param$mtry, ...) :
## Can't have empty classes in y.
## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,
## : There were missing values in resampled performance measures.
## Warning in randomForest.default(x, y, mtry = param$mtry, ...): invalid mtry:
## reset to within valid range
DryBean_TDA_KDE_5.60.5_n5_RfFit0
## Random Forest
##
## 774 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 516, 517, 515
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa
## 50 0.7204175 0.5010763
## 100 0.7145960 0.4886543
## 150 0.7165339 0.4929159
## 200 0.7126354 0.4864193
## 250 0.7184870 0.4932110
## 300 0.7068289 0.4759614
## 350 0.7243010 0.5090073
## 400 0.7126580 0.4850380
## 450 0.7087594 0.4789768
## 500 0.7262390 0.5126513
## 550 0.7126655 0.4851590
## 600 0.7165490 0.4906555
## 650 0.7165339 0.4938777
## 700 0.7126580 0.4846906
## 750 0.7106974 0.4823407
## 800 0.7087518 0.4808751
## 850 0.7204175 0.5010197
## 900 0.7126580 0.4858585
## 950 0.7204175 0.4981472
## 1000 0.7204325 0.4997602
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 500.
DryBean_TDA_KDE_5.60.5_n5_RfFit0$resample
## Accuracy Kappa Resample
## 1 0.7131783 0.4894641 Fold1
## 2 NA NA Fold3
## 3 0.7392996 0.5358385 Fold2
ad_tda_kde_5.60.5_n5_rf_fit0_re<-DryBean_TDA_KDE_5.60.5_n5_RfFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n5_RfFit0)
## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 774 factor numeric
## err.rate 2500 -none- numeric
## confusion 20 -none- numeric
## votes 3096 matrix numeric
## oob.times 774 -none- numeric
## classes 4 -none- character
## importance 16 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 774 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 4 -none- character
## param 1 -none- list
vip(DryBean_TDA_KDE_5.60.5_n5_RfFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n5_RfFit TDA-Assited RF")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n5_RfFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n5_RfFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n5_rf_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n5_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 312 91 374 1028 503 131 568
## HOROZ 0 0 0 0 0 0 0
## SEKER 11 2 1 3 0 462 2
## SIRA 73 63 114 32 75 15 220
##
## Overall Statistics
##
## Accuracy : 0.4191
## 95% CI : (0.4039, 0.4344)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.238
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9671
## Specificity 1.00000 1.00000 1.0000 0.3441
## Pos Pred Value NaN NaN NaN 0.3419
## Neg Pred Value 0.90294 0.96176 0.8801 0.9674
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2520
## Detection Prevalence 0.00000 0.00000 0.0000 0.7370
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6556
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.7599 0.27848
## Specificity 1.0000 0.9945 0.88693
## Pos Pred Value NaN 0.9605 0.37162
## Neg Pred Value 0.8583 0.9594 0.83658
## Prevalence 0.1417 0.1490 0.19363
## Detection Rate 0.0000 0.1132 0.05392
## Detection Prevalence 0.0000 0.1179 0.14510
## Balanced Accuracy 0.5000 0.8772 0.58271
ad_tda_kde_5.60.5_n5_rf_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 312 91 374 1028 503 131 568
## HOROZ 0 0 0 0 0 0 0
## SEKER 11 2 1 3 0 462 2
## SIRA 73 63 114 32 75 15 220
##
## Overall Statistics
##
## Accuracy : 0.4191
## 95% CI : (0.4039, 0.4344)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.238
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9671
## Specificity 1.00000 1.00000 1.0000 0.3441
## Pos Pred Value NaN NaN NaN 0.3419
## Neg Pred Value 0.90294 0.96176 0.8801 0.9674
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2520
## Detection Prevalence 0.00000 0.00000 0.0000 0.7370
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6556
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.7599 0.27848
## Specificity 1.0000 0.9945 0.88693
## Pos Pred Value NaN 0.9605 0.37162
## Neg Pred Value 0.8583 0.9594 0.83658
## Prevalence 0.1417 0.1490 0.19363
## Detection Rate 0.0000 0.1132 0.05392
## Detection Prevalence 0.0000 0.1179 0.14510
## Balanced Accuracy 0.5000 0.8772 0.58271
ad_tda_kde_5.60.5_n5_rf_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.191176e-01 2.380040e-01 4.039174e-01 4.344338e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 6.041558e-107 NaN
ad_tda_kde_5.60.5_n5_rf_cf0_ov_acc<-ad_tda_kde_5.60.5_n5_rf_cf0$overall[1]
ad_tda_kde_5.60.5_n5_rf_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9670743 0.3440504 0.3418690 0.9673812 0.3418690
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.7598684 0.9945276 0.9604990 0.9594332 0.9604990
## Class: SIRA 0.2784810 0.8869301 0.3716216 0.8365826 0.3716216
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.00000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.0000000 NA 0.11985294 0.00000000
## Class: DERMASON 0.9670743 0.5051597 0.26053922 0.25196078
## Class: HOROZ 0.0000000 NA 0.14166667 0.00000000
## Class: SEKER 0.7598684 0.8484848 0.14901961 0.11323529
## Class: SIRA 0.2784810 0.3183792 0.19362745 0.05392157
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.0000000 0.5000000
## Class: DERMASON 0.7370098 0.6555623
## Class: HOROZ 0.0000000 0.5000000
## Class: SEKER 0.1178922 0.8771980
## Class: SIRA 0.1450980 0.5827056
ad_tda_kde_5.60.5_n5_rf_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n5_rf_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_rf_n5_3_fold<-(db_rf_fit_re-ad_tda_kde_5.60.5_n5_rf_fit0_re)
diff_drybean_tda_kde_5.60.5_rf_n5_3_fold
## Accuracy
## 1 0.2090754
## 2 NA
## 3 0.1741407
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n5_3_fold
## $probLeft
## [1] NA
##
## $probRope
## [1] NA
##
## $probRight
## [1] NA
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n5_3_fold_odds.left<-bst_tda_kde_5.60.5_rf.n5_3_fold$probLeft/bst_tda_kde_5.60.5_rf.n5_3_fold$probRight
bst_tda_kde_5.60.5_rf.n5_3_fold_odds.left
## [1] NA
# Bayesian Signed Rank Test
#bsr_tda_kde_5.60.5_rf.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n5_3_fold),-0.01,0.01)
#bsr_tda_kde_5.60.5_rf.n5_3_fold
# Bayesian Correlated Test
#bct_tda_kde_5.60.5_rf.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n5_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.60.5_rf.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf_n5_3_fold))
#bf_tda_kde_5.60.5_rf.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf_n5_3_fold))
### Test set diff
diff_drybean_tda_kde_5.60.5_rf.n5_test<-(db_rf_cf_ov_acc-ad_tda_kde_5.60.5_n5_rf_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_rf.n5_test
## Accuracy
## 0.504902
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n5_test),-0.01,0.01)
bst_tda_kde_5.60.5_rf.n5_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_rf.n5_test_odds.left<-bst_tda_kde_5.60.5_rf.n5_test$probLeft/bst_tda_kde_5.60.5_rf.n5_test$probRight
bst_tda_kde_5.60.5_rf.n5_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_rf.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n5_test),-0.01,0.01)
bsr_tda_kde_5.60.5_rf.n5_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1557
##
## $winRight
## [1] 0.8443
# Bayesian Correlated Test
bct_tda_kde_5.60.5_rf.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_rf.n5_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_rf.n5_test))
#BayesFactor
#bf_tda_kde_5.60.5_rf.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_rf.n5_test)) #bf_tda_kde_5.60.5_rf.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_rf.n5_test))
##Non-TDA-Assisted
svmGrid<-expand.grid(sigma = c(0.1, 1, 10), C = (1:5*0.25))
#Support Vector Machine-Radial Basis
dryBeanSvmFit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
dryBeanSvmFit
## Support Vector Machines with Radial Basis Function Kernel
##
## 9531 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 6354, 6353, 6355
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.9277092 0.9125640
## 0.1 0.50 0.9297028 0.9149672
## 0.1 0.75 0.9297026 0.9149615
## 0.1 1.00 0.9299124 0.9152168
## 0.1 1.25 0.9311713 0.9167360
## 1.0 0.25 0.8984359 0.8770496
## 1.0 0.50 0.9128105 0.8945247
## 1.0 0.75 0.9164825 0.8989732
## 1.0 1.00 0.9198400 0.9030263
## 1.0 1.25 0.9198402 0.9030237
## 10.0 0.25 0.3500151 0.1314073
## 10.0 0.50 0.4503195 0.2748202
## 10.0 0.75 0.5300599 0.3873530
## 10.0 1.00 0.6081214 0.4957386
## 10.0 1.25 0.6314138 0.5279090
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
dryBeanSvmFit$resample
## Accuracy Kappa Resample
## 1 0.9285489 0.913598 Fold1
## 2 0.9294710 0.914659 Fold3
## 3 0.9354940 0.921951 Fold2
db_svm_fit_re<-dryBeanSvmFit$resample[1]
summary(dryBeanSvmFit)
## Length Class Mode
## 1 ksvm S4
#vip(dryBeanSvmFit, 25) + ggtitle("non-TDA-Assited Svm")
# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanSvmFit, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_svm_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_svm_cf
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 346 0 11 0 2 3 4
## BOMBAY 0 156 0 0 0 0 0
## CALI 29 0 462 0 10 0 0
## DERMASON 0 0 0 993 4 12 71
## HOROZ 2 0 8 2 558 1 11
## SEKER 4 0 1 11 0 576 11
## SIRA 15 0 7 57 4 16 693
##
## Overall Statistics
##
## Accuracy : 0.9275
## 95% CI : (0.9191, 0.9352)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9122
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.87374 1.00000 0.9448 0.9341
## Specificity 0.99457 1.00000 0.9891 0.9712
## Pos Pred Value 0.94536 1.00000 0.9222 0.9194
## Neg Pred Value 0.98654 1.00000 0.9925 0.9767
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08480 0.03824 0.1132 0.2434
## Detection Prevalence 0.08971 0.03824 0.1228 0.2647
## Balanced Accuracy 0.93415 1.00000 0.9670 0.9527
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9654 0.9474 0.8772
## Specificity 0.9931 0.9922 0.9699
## Pos Pred Value 0.9588 0.9552 0.8750
## Neg Pred Value 0.9943 0.9908 0.9705
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1368 0.1412 0.1699
## Detection Prevalence 0.1426 0.1478 0.1941
## Balanced Accuracy 0.9793 0.9698 0.9236
db_svm_cf$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9274510 0.9122032 0.9190588 0.9352246 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_svm_cf_ov_acc<-db_svm_cf$overall[1]
db_svm_cf$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8737374 0.9945711 0.9453552 0.9865374 0.9453552
## Class: BOMBAY 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## Class: CALI 0.9447853 0.9891395 0.9221557 0.9924560 0.9221557
## Class: DERMASON 0.9341486 0.9711634 0.9194444 0.9766667 0.9194444
## Class: HOROZ 0.9653979 0.9931468 0.9587629 0.9942824 0.9587629
## Class: SEKER 0.9473684 0.9922235 0.9552239 0.9907967 0.9552239
## Class: SIRA 0.8772152 0.9699088 0.8750000 0.9704988 0.8750000
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8737374 0.9081365 0.09705882 0.08480392
## Class: BOMBAY 1.0000000 1.0000000 0.03823529 0.03823529
## Class: CALI 0.9447853 0.9333333 0.11985294 0.11323529
## Class: DERMASON 0.9341486 0.9267382 0.26053922 0.24338235
## Class: HOROZ 0.9653979 0.9620690 0.14166667 0.13676471
## Class: SEKER 0.9473684 0.9512799 0.14901961 0.14117647
## Class: SIRA 0.8772152 0.8761062 0.19362745 0.16985294
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.08970588 0.9341542
## Class: BOMBAY 0.03823529 1.0000000
## Class: CALI 0.12279412 0.9669624
## Class: DERMASON 0.26470588 0.9526560
## Class: HOROZ 0.14264706 0.9792723
## Class: SEKER 0.14779412 0.9697960
## Class: SIRA 0.19411765 0.9235620
db_svm_cf_pr_rec_f1<-db_svm_cf$byClass[5:7]
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_PC_5.60.5_n1_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n1.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_PC_5.60.5_n1_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 6835 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4557, 4555, 4558
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.9106090 0.8525204728
## 0.1 0.50 0.9126561 0.8557647610
## 0.1 0.75 0.9135333 0.8572004449
## 0.1 1.00 0.9139729 0.8579434295
## 0.1 1.25 0.9145579 0.8589339182
## 1.0 0.25 0.8645229 0.7683515269
## 1.0 0.50 0.8835420 0.8038961152
## 1.0 0.75 0.8892479 0.8148415388
## 1.0 1.00 0.8930512 0.8217918544
## 1.0 1.25 0.8931978 0.8223875190
## 10.0 0.25 0.5149965 0.0003681942
## 10.0 0.50 0.5265545 0.0292875825
## 10.0 0.75 0.5536213 0.0959021520
## 10.0 1.00 0.5855131 0.1728353181
## 10.0 1.25 0.6092155 0.2297499248
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_PC_5.60.5_n1_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.9117647 0.8550654 Fold1
## 2 0.9174352 0.8632422 Fold3
## 3 0.9144737 0.8584941 Fold2
db_tda_pc_5.60.5_n1_svm_fit_re<-DryBean_TDA_PC_5.60.5_n1_SvmFit0 $resample[1]
summary(DryBean_TDA_PC_5.60.5_n1_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_PC_5.60.5_n1_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n1_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_PC_5.60.5_n1_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n1_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n1_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n1_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 2 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 331 156 481 1020 578 13 160
## HOROZ 0 0 0 0 0 0 0
## SEKER 58 0 6 12 0 584 59
## SIRA 5 0 2 31 0 11 571
##
## Overall Statistics
##
## Accuracy : 0.5336
## 95% CI : (0.5181, 0.549)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3938
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.0050505 0.00000 0.0000 0.9595
## Specificity 1.0000000 1.00000 1.0000 0.4302
## Pos Pred Value 1.0000000 NaN NaN 0.3724
## Neg Pred Value 0.9033840 0.96176 0.8801 0.9679
## Prevalence 0.0970588 0.03824 0.1199 0.2605
## Detection Rate 0.0004902 0.00000 0.0000 0.2500
## Detection Prevalence 0.0004902 0.00000 0.0000 0.6713
## Balanced Accuracy 0.5025253 0.50000 0.5000 0.6949
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9605 0.7228
## Specificity 1.0000 0.9611 0.9851
## Pos Pred Value NaN 0.8122 0.9210
## Neg Pred Value 0.8583 0.9929 0.9367
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1431 0.1400
## Detection Prevalence 0.0000 0.1762 0.1520
## Balanced Accuracy 0.5000 0.9608 0.8539
db_tda_pc_5.60.5_n1_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 2 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 331 156 481 1020 578 13 160
## HOROZ 0 0 0 0 0 0 0
## SEKER 58 0 6 12 0 584 59
## SIRA 5 0 2 31 0 11 571
##
## Overall Statistics
##
## Accuracy : 0.5336
## 95% CI : (0.5181, 0.549)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.3938
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.0050505 0.00000 0.0000 0.9595
## Specificity 1.0000000 1.00000 1.0000 0.4302
## Pos Pred Value 1.0000000 NaN NaN 0.3724
## Neg Pred Value 0.9033840 0.96176 0.8801 0.9679
## Prevalence 0.0970588 0.03824 0.1199 0.2605
## Detection Rate 0.0004902 0.00000 0.0000 0.2500
## Detection Prevalence 0.0004902 0.00000 0.0000 0.6713
## Balanced Accuracy 0.5025253 0.50000 0.5000 0.6949
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9605 0.7228
## Specificity 1.0000 0.9611 0.9851
## Pos Pred Value NaN 0.8122 0.9210
## Neg Pred Value 0.8583 0.9929 0.9367
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1431 0.1400
## Detection Prevalence 0.0000 0.1762 0.1520
## Balanced Accuracy 0.5000 0.9608 0.8539
db_tda_pc_5.60.5_n1_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 5.335784e-01 3.937550e-01 5.181289e-01 5.489799e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 2.302491e-299 NaN
db_tda_pc_5.60.5_n1_db_svm_cf0_ov_acc<-db_tda_pc_5.60.5_n1_db_svm_cf0$overall[1]
db_tda_pc_5.60.5_n1_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.005050505 1.0000000 1.0000000 0.9033840 1.0000000
## Class: BOMBAY 0.000000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.000000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.959548448 0.4302287 0.3723987 0.9679344 0.3723987
## Class: HOROZ 0.000000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.960526316 0.9611175 0.8122392 0.9928593 0.8122392
## Class: SIRA 0.722784810 0.9851064 0.9209677 0.9367052 0.9209677
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.005050505 0.01005025 0.09705882 0.0004901961
## Class: BOMBAY 0.000000000 NA 0.03823529 0.0000000000
## Class: CALI 0.000000000 NA 0.11985294 0.0000000000
## Class: DERMASON 0.959548448 0.53655971 0.26053922 0.2500000000
## Class: HOROZ 0.000000000 NA 0.14166667 0.0000000000
## Class: SEKER 0.960526316 0.88018086 0.14901961 0.1431372549
## Class: SIRA 0.722784810 0.80992908 0.19362745 0.1399509804
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0004901961 0.5025253
## Class: BOMBAY 0.0000000000 0.5000000
## Class: CALI 0.0000000000 0.5000000
## Class: DERMASON 0.6713235294 0.6948886
## Class: HOROZ 0.0000000000 0.5000000
## Class: SEKER 0.1762254902 0.9608219
## Class: SIRA 0.1519607843 0.8539456
db_tda_pc_5.60.5_n1_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n1_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_svm_n1_3_fold<-(db_svm_fit_re - db_tda_pc_5.60.5_n1_svm_fit_re)
diff_drybean_tda_pca_5.60.5_svm_n1_3_fold
## Accuracy
## 1 0.01678424
## 2 0.01203581
## 3 0.02102034
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n1_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n1_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.09173333
##
## $winRight
## [1] 0.9082667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n1_3_fold
## $left
## [1] 0.006220449
##
## $rope
## [1] 0.07278914
##
## $right
## [1] 0.9209904
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold))
#bf_tda_pca_5.60.5_rf.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_svm_n1_3_fold)
## t = 6.4021, df = 2, p-value = 0.02354
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.005448016 0.027778909
## sample estimates:
## mean of x
## 0.01661346
### Test set diff
diff_drybean_tda_pca_5.60.5_svm.n1_test<-(db_svm_cf_ov_acc - db_tda_pc_5.60.5_n1_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_svm.n1_test
## Accuracy
## 0.3938725
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n1_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n1_test$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n1_test$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1586667
##
## $winRight
## [1] 0.8413333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_svm.n1_test)))
#BayesFactor
#bf_tda_pca_5.60.5_svm.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm.n1_test)) #bf_tda_pca_5.60.5_svm.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n1_test))
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node2
DryBean_TDA_PC_5.60.5_n2_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n2.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_PC_5.60.5_n2_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 8024 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5349, 5350, 5349
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.8904552 0.856994446
## 0.1 0.50 0.8921997 0.859308951
## 0.1 0.75 0.8933215 0.860790502
## 0.1 1.00 0.8930722 0.860511409
## 0.1 1.25 0.8936952 0.861363079
## 1.0 0.25 0.8610430 0.818387419
## 1.0 0.50 0.8769952 0.839561228
## 1.0 0.75 0.8812320 0.845157612
## 1.0 1.00 0.8822290 0.846489251
## 1.0 1.25 0.8818551 0.846034487
## 10.0 0.25 0.3311317 0.005034164
## 10.0 0.50 0.3885836 0.094429567
## 10.0 0.75 0.4525166 0.195280745
## 10.0 1.00 0.4922727 0.257703232
## 10.0 1.25 0.5107175 0.287513996
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_PC_5.60.5_n2_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.8968224 0.8653942 Fold1
## 2 0.8800000 0.8433578 Fold3
## 3 0.9042633 0.8753372 Fold2
db_tda_pc_5.60.5_n2_svm_fit_re<-DryBean_TDA_PC_5.60.5_n2_SvmFit0 $resample[1]
summary(DryBean_TDA_PC_5.60.5_n2_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_PC_5.60.5_n2_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n2_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_PC_5.60.5_n2_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n2_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n2_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n2_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 361 12 90 0 4 15 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 20 0 369 0 6 0 0
## DERMASON 0 144 0 1009 6 67 76
## HOROZ 2 0 23 2 558 0 10
## SEKER 3 0 1 7 0 511 10
## SIRA 10 0 6 45 4 15 691
##
## Overall Statistics
##
## Accuracy : 0.8576
## 95% CI : (0.8465, 0.8682)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8257
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.91162 0.00000 0.75460 0.9492
## Specificity 0.96634 1.00000 0.99276 0.9029
## Pos Pred Value 0.74433 NaN 0.93418 0.7750
## Neg Pred Value 0.99026 0.96176 0.96744 0.9806
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.08848 0.00000 0.09044 0.2473
## Detection Prevalence 0.11887 0.00000 0.09681 0.3191
## Balanced Accuracy 0.93898 0.50000 0.87368 0.9260
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9654 0.8405 0.8747
## Specificity 0.9894 0.9940 0.9757
## Pos Pred Value 0.9378 0.9605 0.8962
## Neg Pred Value 0.9943 0.9727 0.9701
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1368 0.1252 0.1694
## Detection Prevalence 0.1458 0.1304 0.1890
## Balanced Accuracy 0.9774 0.9172 0.9252
db_tda_pc_5.60.5_n2_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 361 12 90 0 4 15 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 20 0 369 0 6 0 0
## DERMASON 0 144 0 1009 6 67 76
## HOROZ 2 0 23 2 558 0 10
## SEKER 3 0 1 7 0 511 10
## SIRA 10 0 6 45 4 15 691
##
## Overall Statistics
##
## Accuracy : 0.8576
## 95% CI : (0.8465, 0.8682)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8257
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.91162 0.00000 0.75460 0.9492
## Specificity 0.96634 1.00000 0.99276 0.9029
## Pos Pred Value 0.74433 NaN 0.93418 0.7750
## Neg Pred Value 0.99026 0.96176 0.96744 0.9806
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.08848 0.00000 0.09044 0.2473
## Detection Prevalence 0.11887 0.00000 0.09681 0.3191
## Balanced Accuracy 0.93898 0.50000 0.87368 0.9260
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9654 0.8405 0.8747
## Specificity 0.9894 0.9940 0.9757
## Pos Pred Value 0.9378 0.9605 0.8962
## Neg Pred Value 0.9943 0.9727 0.9701
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1368 0.1252 0.1694
## Detection Prevalence 0.1458 0.1304 0.1890
## Balanced Accuracy 0.9774 0.9172 0.9252
db_tda_pc_5.60.5_n2_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8575980 0.8257090 0.8464965 0.8681847 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n2_db_svm_cf0_ov_acc<-db_tda_pc_5.60.5_n2_db_svm_cf0$overall[1]
db_tda_pc_5.60.5_n2_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.9116162 0.9663409 0.7443299 0.9902643 0.7443299
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.7546012 0.9927597 0.9341772 0.9674355 0.9341772
## Class: DERMASON 0.9492004 0.9028837 0.7749616 0.9805616 0.7749616
## Class: HOROZ 0.9653979 0.9894346 0.9378151 0.9942611 0.9378151
## Class: SEKER 0.8404605 0.9939516 0.9605263 0.9726607 0.9605263
## Class: SIRA 0.8746835 0.9756839 0.8962387 0.9700816 0.8962387
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9116162 0.8195233 0.09705882 0.08848039
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.7546012 0.8348416 0.11985294 0.09044118
## Class: DERMASON 0.9492004 0.8532770 0.26053922 0.24730392
## Class: HOROZ 0.9653979 0.9514066 0.14166667 0.13676471
## Class: SEKER 0.8404605 0.8964912 0.14901961 0.12524510
## Class: SIRA 0.8746835 0.8853299 0.19362745 0.16936275
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.11887255 0.9389785
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.09681373 0.8736805
## Class: DERMASON 0.31911765 0.9260420
## Class: HOROZ 0.14583333 0.9774163
## Class: SEKER 0.13039216 0.9172061
## Class: SIRA 0.18897059 0.9251837
db_tda_pc_5.60.5_n2_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n2_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_svm_n2_3_fold<-(db_svm_fit_re - db_tda_pc_5.60.5_n2_svm_fit_re)
diff_drybean_tda_pca_5.60.5_svm_n2_3_fold
## Accuracy
## 1 0.03172652
## 2 0.04947103
## 3 0.03123075
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n2_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n2_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0094
##
## $winRight
## [1] 0.9906
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n2_3_fold
## $left
## [1] 0.01031659
##
## $rope
## [1] 0.01872413
##
## $right
## [1] 0.9709593
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold))
#bf_tda_pca_5.60.5_rf.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_svm_n2_3_fold)
## t = 6.2469, df = 2, p-value = 0.02468
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.01166373 0.06328846
## sample estimates:
## mean of x
## 0.0374761
### Test set diff
diff_drybean_tda_pca_5.60.5_svm.n2_test<-(db_svm_cf_ov_acc - db_tda_pc_5.60.5_n2_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_svm.n2_test
## Accuracy
## 0.06985294
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n2_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n2_test$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n2_test$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1592667
##
## $winRight
## [1] 0.8407333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_svm.n2_test)))
#BayesFactor
#bf_tda_pca_5.60.5_svm.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm.n2_test)) #bf_tda_pca_5.60.5_svm.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n2_test))
##Node3
DryBean_TDA_PC_5.60.5_n3_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n3.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_PC_5.60.5_n3_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 5008 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 3339, 3339, 3338
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.8009705 0.75003112
## 0.1 0.50 0.8021685 0.75163104
## 0.1 0.75 0.8035661 0.75355578
## 0.1 1.00 0.8031666 0.75308351
## 0.1 1.25 0.8039649 0.75393389
## 1.0 0.25 0.7722167 0.71133345
## 1.0 0.50 0.7861945 0.73084032
## 1.0 0.75 0.7867932 0.73134489
## 1.0 1.00 0.7871928 0.73192630
## 1.0 1.25 0.7875922 0.73256122
## 10.0 0.25 0.2518473 0.00000000
## 10.0 0.50 0.2546430 0.00412182
## 10.0 0.75 0.3013690 0.06863355
## 10.0 1.00 0.4010095 0.20394619
## 10.0 1.25 0.4253706 0.23783452
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_PC_5.60.5_n3_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.9478730 0.9245344 Fold1
## 2 0.5479042 0.4586663 Fold3
## 3 0.9161174 0.8786009 Fold2
db_tda_pc_5.60.5_n3_svm_fit_re<-DryBean_TDA_PC_5.60.5_n3_SvmFit0 $resample[1]
summary(DryBean_TDA_PC_5.60.5_n3_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_PC_5.60.5_n3_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n3_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_PC_5.60.5_n3_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n3_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n3_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.60.5_n3_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 357 0 12 4 1 23 128
## BOMBAY 0 61 0 0 0 0 0
## CALI 29 0 465 0 9 0 1
## DERMASON 0 0 0 0 0 0 0
## HOROZ 7 95 8 1059 566 581 340
## SEKER 0 0 0 0 0 1 0
## SIRA 3 0 4 0 2 3 321
##
## Overall Statistics
##
## Accuracy : 0.4341
## 95% CI : (0.4188, 0.4494)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.345
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.90152 0.39103 0.9509 0.0000
## Specificity 0.95440 1.00000 0.9891 1.0000
## Pos Pred Value 0.68000 1.00000 0.9226 NaN
## Neg Pred Value 0.98903 0.97636 0.9933 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08750 0.01495 0.1140 0.0000
## Detection Prevalence 0.12868 0.01495 0.1235 0.0000
## Balanced Accuracy 0.92796 0.69551 0.9700 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9792 0.0016447 0.40633
## Specificity 0.4032 1.0000000 0.99635
## Pos Pred Value 0.2131 1.0000000 0.96396
## Neg Pred Value 0.9916 0.8511890 0.87483
## Prevalence 0.1417 0.1490196 0.19363
## Detection Rate 0.1387 0.0002451 0.07868
## Detection Prevalence 0.6510 0.0002451 0.08162
## Balanced Accuracy 0.6912 0.5008224 0.70134
db_tda_pc_5.60.5_n3_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 357 0 12 4 1 23 128
## BOMBAY 0 61 0 0 0 0 0
## CALI 29 0 465 0 9 0 1
## DERMASON 0 0 0 0 0 0 0
## HOROZ 7 95 8 1059 566 581 340
## SEKER 0 0 0 0 0 1 0
## SIRA 3 0 4 0 2 3 321
##
## Overall Statistics
##
## Accuracy : 0.4341
## 95% CI : (0.4188, 0.4494)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.345
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.90152 0.39103 0.9509 0.0000
## Specificity 0.95440 1.00000 0.9891 1.0000
## Pos Pred Value 0.68000 1.00000 0.9226 NaN
## Neg Pred Value 0.98903 0.97636 0.9933 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08750 0.01495 0.1140 0.0000
## Detection Prevalence 0.12868 0.01495 0.1235 0.0000
## Balanced Accuracy 0.92796 0.69551 0.9700 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9792 0.0016447 0.40633
## Specificity 0.4032 1.0000000 0.99635
## Pos Pred Value 0.2131 1.0000000 0.96396
## Neg Pred Value 0.9916 0.8511890 0.87483
## Prevalence 0.1417 0.1490196 0.19363
## Detection Rate 0.1387 0.0002451 0.07868
## Detection Prevalence 0.6510 0.0002451 0.08162
## Balanced Accuracy 0.6912 0.5008224 0.70134
db_tda_pc_5.60.5_n3_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.340686e-01 3.450411e-01 4.187896e-01 4.494421e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 9.033230e-127 NaN
db_tda_pc_5.60.5_n3_db_svm_cf0_ov_acc<-db_tda_pc_5.60.5_n3_db_svm_cf0$overall[1]
db_tda_pc_5.60.5_n3_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.901515152 0.9543974 0.6800000 0.9890295 0.6800000
## Class: BOMBAY 0.391025641 1.0000000 1.0000000 0.9763623 1.0000000
## Class: CALI 0.950920245 0.9891395 0.9226190 0.9932886 0.9226190
## Class: DERMASON 0.000000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.979238754 0.4031982 0.2131024 0.9915730 0.2131024
## Class: SEKER 0.001644737 1.0000000 1.0000000 0.8511890 1.0000000
## Class: SIRA 0.406329114 0.9963526 0.9639640 0.8748332 0.9639640
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.901515152 0.775244300 0.09705882 0.087500000
## Class: BOMBAY 0.391025641 0.562211982 0.03823529 0.014950980
## Class: CALI 0.950920245 0.936555891 0.11985294 0.113970588
## Class: DERMASON 0.000000000 NA 0.26053922 0.000000000
## Class: HOROZ 0.979238754 0.350030921 0.14166667 0.138725490
## Class: SEKER 0.001644737 0.003284072 0.14901961 0.000245098
## Class: SIRA 0.406329114 0.571682992 0.19362745 0.078676471
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.128676471 0.9279563
## Class: BOMBAY 0.014950980 0.6955128
## Class: CALI 0.123529412 0.9700299
## Class: DERMASON 0.000000000 0.5000000
## Class: HOROZ 0.650980392 0.6912185
## Class: SEKER 0.000245098 0.5008224
## Class: SIRA 0.081617647 0.7013408
db_tda_pc_5.60.5_n3_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n3_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_svm_n3_3_fold<-(db_svm_fit_re - db_tda_pc_5.60.5_n3_svm_fit_re)
diff_drybean_tda_pca_5.60.5_svm_n3_3_fold
## Accuracy
## 1 -0.01932403
## 2 0.38156684
## 3 0.01937659
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n3_3_fold
## $probLeft
## [1] 0.25
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n3_3_fold_odds.left
## [1] 0.5
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n3_3_fold
## $winLeft
## [1] 0.08196667
##
## $winRope
## [1] 0.2419333
##
## $winRight
## [1] 0.6761
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n3_3_fold
## $left
## [1] 0.2251276
##
## $rope
## [1] 0.02984633
##
## $right
## [1] 0.745026
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold))
#bf_tda_pca_5.60.5_rf.n3_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_svm_n3_3_fold)
## t = 0.99637, df = 2, p-value = 0.424
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.4221129 0.6765258
## sample estimates:
## mean of x
## 0.1272065
### Test set diff
diff_drybean_tda_pca_5.60.5_svm.n3_test<-(db_svm_cf_ov_acc - db_tda_pc_5.60.5_n3_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_svm.n3_test
## Accuracy
## 0.4933824
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n3_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n3_test$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n3_test$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n3_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1625667
##
## $winRight
## [1] 0.8374333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_svm.n3_test)))
#BayesFactor
#bf_tda_pca_5.60.5_svm.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm.n3_test)) #bf_tda_pca_5.60.5_svm.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n3_test))
##Node4
DryBean_TDA_PC_5.60.5_n4_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n4.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_PC_5.60.5_n4_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 894 samples
## 16 predictor
## 4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 595, 597, 596
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.9832251 0.97273865
## 0.1 0.50 0.9821065 0.97093742
## 0.1 0.75 0.9843512 0.97464466
## 0.1 1.00 0.9832251 0.97279842
## 0.1 1.25 0.9843399 0.97460243
## 1.0 0.25 0.8423032 0.71760681
## 1.0 0.50 0.8959723 0.82027897
## 1.0 0.75 0.9328704 0.88721852
## 1.0 1.00 0.9463046 0.91075313
## 1.0 1.25 0.9463046 0.91075313
## 10.0 0.25 0.5055929 0.00000000
## 10.0 0.50 0.5055929 0.00000000
## 10.0 0.75 0.5167599 0.02632514
## 10.0 1.00 0.5603731 0.12984849
## 10.0 1.25 0.5861041 0.18792896
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 0.75.
DryBean_TDA_PC_5.60.5_n4_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.9799331 0.9675922 Fold1
## 2 0.9898990 0.9835818 Fold2
## 3 0.9832215 0.9727600 Fold3
db_tda_pc_5.60.5_n4_svm_fit_re<-DryBean_TDA_PC_5.60.5_n4_SvmFit0 $resample[1]
summary(DryBean_TDA_PC_5.60.5_n4_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_PC_5.60.5_n4_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n4_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_PC_5.60.5_n4_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n4_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n4_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n4_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 220 0 5 0 1 0 0
## BOMBAY 23 156 5 0 0 0 0
## CALI 43 0 459 0 37 0 5
## DERMASON 0 0 0 0 0 0 0
## HOROZ 110 0 20 1063 540 608 785
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.337
## 95% CI : (0.3225, 0.3517)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2365
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.55556 1.00000 0.9387 0.0000
## Specificity 0.99837 0.99286 0.9763 1.0000
## Pos Pred Value 0.97345 0.84783 0.8437 NaN
## Neg Pred Value 0.95433 1.00000 0.9915 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.05392 0.03824 0.1125 0.0000
## Detection Prevalence 0.05539 0.04510 0.1333 0.0000
## Balanced Accuracy 0.77696 0.99643 0.9575 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9343 0.000 0.0000
## Specificity 0.2616 1.000 1.0000
## Pos Pred Value 0.1727 NaN NaN
## Neg Pred Value 0.9602 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1324 0.000 0.0000
## Detection Prevalence 0.7662 0.000 0.0000
## Balanced Accuracy 0.5979 0.500 0.5000
db_tda_pc_5.60.5_n4_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 220 0 5 0 1 0 0
## BOMBAY 23 156 5 0 0 0 0
## CALI 43 0 459 0 37 0 5
## DERMASON 0 0 0 0 0 0 0
## HOROZ 110 0 20 1063 540 608 785
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.337
## 95% CI : (0.3225, 0.3517)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2365
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.55556 1.00000 0.9387 0.0000
## Specificity 0.99837 0.99286 0.9763 1.0000
## Pos Pred Value 0.97345 0.84783 0.8437 NaN
## Neg Pred Value 0.95433 1.00000 0.9915 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.05392 0.03824 0.1125 0.0000
## Detection Prevalence 0.05539 0.04510 0.1333 0.0000
## Balanced Accuracy 0.77696 0.99643 0.9575 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9343 0.000 0.0000
## Specificity 0.2616 1.000 1.0000
## Pos Pred Value 0.1727 NaN NaN
## Neg Pred Value 0.9602 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1324 0.000 0.0000
## Detection Prevalence 0.7662 0.000 0.0000
## Balanced Accuracy 0.5979 0.500 0.5000
db_tda_pc_5.60.5_n4_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.370098e-01 2.365183e-01 3.225041e-01 3.517493e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 1.537231e-27 NaN
db_tda_pc_5.60.5_n4_db_svm_cf0_ov_acc<-db_tda_pc_5.60.5_n4_db_svm_cf0$overall[1]
db_tda_pc_5.60.5_n4_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.5555556 0.9983713 0.9734513 0.9543332 0.9734513
## Class: BOMBAY 1.0000000 0.9928644 0.8478261 1.0000000 0.8478261
## Class: CALI 0.9386503 0.9763297 0.8437500 0.9915158 0.8437500
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.9342561 0.2615648 0.1727447 0.9601677 0.1727447
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.5555556 0.7073955 0.09705882 0.05392157
## Class: BOMBAY 1.0000000 0.9176471 0.03823529 0.03823529
## Class: CALI 0.9386503 0.8886738 0.11985294 0.11250000
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.9342561 0.2915767 0.14166667 0.13235294
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.05539216 0.7769634
## Class: BOMBAY 0.04509804 0.9964322
## Class: CALI 0.13333333 0.9574900
## Class: DERMASON 0.00000000 0.5000000
## Class: HOROZ 0.76617647 0.5979104
## Class: SEKER 0.00000000 0.5000000
## Class: SIRA 0.00000000 0.5000000
db_tda_pc_5.60.5_n4_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n4_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_svm_n4_3_fold<-(db_svm_fit_re - db_tda_pc_5.60.5_n4_svm_fit_re)
diff_drybean_tda_pca_5.60.5_svm_n4_3_fold
## Accuracy
## 1 -0.05138416
## 2 -0.06042796
## 3 -0.04772746
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n4_3_fold
## $winLeft
## [1] 0.9914667
##
## $winRope
## [1] 0.008533333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n4_3_fold
## $left
## [1] 0.9949821
##
## $rope
## [1] 0.002655173
##
## $right
## [1] 0.002362758
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold))
#bf_tda_pca_5.60.5_rf.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_svm_n4_3_fold)
## t = -14.089, df = 2, p-value = 0.005
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.06942089 -0.03693883
## sample estimates:
## mean of x
## -0.05317986
### Test set diff
diff_drybean_tda_pca_5.60.5_svm.n4_test<-(db_svm_cf_ov_acc - db_tda_pc_5.60.5_n4_db_svm_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_svm.n4_test
## Accuracy
## 0.5904412
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_svm.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_svm.n4_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n4_test$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n4_test$probRight
bst_dbf_db_tda_pca_5.60.5_svm.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_svm.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_svm.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1605
##
## $winRight
## [1] 0.8395
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_svm.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_svm.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_svm.n4_test)))
#BayesFactor
#bf_tda_pca_5.60.5_svm.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm.n4_test)) #bf_tda_pca_5.60.5_svm.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n4_test))
##Node5
#DryBean_TDA_PC_5.60.5_n5_SvmFit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.60.5.n5.vec,
# Importance = T,
# method = 'svmRadial',
# trControl = fitControl,
# tuneGrid = svmGrid, preProc = c('center','scale'),
# metric='Accuracy')
#DryBean_TDA_PC_5.60.5_n5_SvmFit0
#DryBean_TDA_PC_5.60.5_n5_SvmFit0$resample
#db_tda_pc_5.60.5_n5_svm_fit_re<-DryBean_TDA_PC_5.60.5_n5_SvmFit0 $resample[1]
#summary(DryBean_TDA_PC_5.60.5_n5_SvmFit0)
#vip(DryBean_TDA_PC_5.60.5_n5_SvmFit0,25) + ggtitle("Adult_TDA_PCA_5.60.5_n5_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_PC_5.60.5_n5_SvmFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.60.5_n5_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.60.5_n5_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.60.5_n5_db_svm_cf0
#db_tda_pc_5.60.5_n5_db_svm_cf0
#db_tda_pc_5.60.5_n5_db_svm_cf0$overall
#db_tda_pc_5.60.5_n5_db_svm_cf0_ov_acc<-db_tda_pc_5.60.5_n5_db_svm_cf0$overall[1]
#db_tda_pc_5.60.5_n5_db_svm_cf0$byClass
#db_tda_pc_5.60.5_n5_db_svm_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n5_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_pca_5.60.5_svm_n5_3_fold<-(db_svm_fit_re - db_tda_pc_5.60.5_n5_svm_fit_re)
#diff_drybean_tda_pca_5.60.5_svm_n5_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_svm.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_svm.n5_3_fold
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_svm.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.60.5_svm.n5_3_fold_odds.left
# Bayesian Signed Rank Test
##bsr_dbf_db_tda_pca_5.60.5_svm.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_svm.n5_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_svm.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_svm.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_svm_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_rf.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm_n5_3_fold))
#bf_tda_pca_5.60.5_rf.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm_n5_3_fold))
### Test set diff
#diff_drybean_tda_pca_5.60.5_svm.n5_test<-(db_svm_cf_ov_acc - db_tda_pc_5.60.5_n5_db_svm_cf0_ov_acc)
#diff_drybean_tda_pca_5.60.5_svm.n5_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_svm.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_svm.n5_test
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_svm.n5_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_svm.n5_test$probLeft/bst_dbf_db_tda_pca_5.60.5_svm.n5_test$probRight
#bst_dbf_db_tda_pca_5.60.5_svm.n5_test_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_svm.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_svm.n5_test
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_svm.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_svm.n5_test
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_svm.n5_test)))
#BayesFactor
#bf_tda_pca_5.60.5_svm.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_svm.n5_test)) #bf_tda_pca_5.60.5_svm.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_svm.n5_test))
#With TDA KDE filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_KDE_5.60.5_n1_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n1.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_KDE_5.60.5_n1_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 7503 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5002, 5001, 5003
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.9502873 0.94009439
## 0.1 0.50 0.9513534 0.94138944
## 0.1 0.75 0.9514867 0.94155689
## 0.1 1.00 0.9528191 0.94316330
## 0.1 1.25 0.9528191 0.94316484
## 1.0 0.25 0.9220306 0.90561266
## 1.0 0.50 0.9342930 0.92059761
## 1.0 0.75 0.9404232 0.92807209
## 1.0 1.00 0.9436215 0.93196080
## 1.0 1.25 0.9434885 0.93180782
## 10.0 0.25 0.2822880 0.07457811
## 10.0 0.50 0.4479573 0.29637229
## 10.0 0.75 0.5616449 0.44593413
## 10.0 1.00 0.6457433 0.55541192
## 10.0 1.25 0.6656020 0.58140156
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.
DryBean_TDA_KDE_5.60.5_n1_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.9532187 0.9436318 Fold1
## 2 0.9516387 0.9417590 Fold2
## 3 0.9536000 0.9440991 Fold3
ad_tda_kde_5.60.5_n1_svm_fit_re<-DryBean_TDA_KDE_5.60.5_n1_SvmFit0 $resample[1]
summary(DryBean_TDA_PC_5.60.5_n1_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_KDE_5.60.5_n1_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n1_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n1_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n1_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n1_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
ad_tda_kde_5.60.5_n1_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 350 0 11 1 2 2 4
## BOMBAY 0 156 0 0 0 0 0
## CALI 25 0 462 0 9 0 0
## DERMASON 0 0 0 835 2 3 21
## HOROZ 3 0 8 2 559 0 10
## SEKER 5 0 1 27 0 585 11
## SIRA 13 0 7 198 6 18 744
##
## Overall Statistics
##
## Accuracy : 0.9047
## 95% CI : (0.8952, 0.9135)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8852
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.88384 1.00000 0.9448 0.7855
## Specificity 0.99457 1.00000 0.9905 0.9914
## Pos Pred Value 0.94595 1.00000 0.9315 0.9698
## Neg Pred Value 0.98760 1.00000 0.9925 0.9292
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08578 0.03824 0.1132 0.2047
## Detection Prevalence 0.09069 0.03824 0.1216 0.2110
## Balanced Accuracy 0.93920 1.00000 0.9677 0.8884
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9671 0.9622 0.9418
## Specificity 0.9934 0.9873 0.9264
## Pos Pred Value 0.9605 0.9300 0.7546
## Neg Pred Value 0.9946 0.9933 0.9851
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1370 0.1434 0.1824
## Detection Prevalence 0.1426 0.1542 0.2417
## Balanced Accuracy 0.9803 0.9747 0.9341
ad_tda_kde_5.60.5_n1_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 350 0 11 1 2 2 4
## BOMBAY 0 156 0 0 0 0 0
## CALI 25 0 462 0 9 0 0
## DERMASON 0 0 0 835 2 3 21
## HOROZ 3 0 8 2 559 0 10
## SEKER 5 0 1 27 0 585 11
## SIRA 13 0 7 198 6 18 744
##
## Overall Statistics
##
## Accuracy : 0.9047
## 95% CI : (0.8952, 0.9135)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8852
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.88384 1.00000 0.9448 0.7855
## Specificity 0.99457 1.00000 0.9905 0.9914
## Pos Pred Value 0.94595 1.00000 0.9315 0.9698
## Neg Pred Value 0.98760 1.00000 0.9925 0.9292
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08578 0.03824 0.1132 0.2047
## Detection Prevalence 0.09069 0.03824 0.1216 0.2110
## Balanced Accuracy 0.93920 1.00000 0.9677 0.8884
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9671 0.9622 0.9418
## Specificity 0.9934 0.9873 0.9264
## Pos Pred Value 0.9605 0.9300 0.7546
## Neg Pred Value 0.9946 0.9933 0.9851
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1370 0.1434 0.1824
## Detection Prevalence 0.1426 0.1542 0.2417
## Balanced Accuracy 0.9803 0.9747 0.9341
ad_tda_kde_5.60.5_n1_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9046569 0.8851577 0.8952306 0.9134989 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
ad_tda_kde_5.60.5_n1_db_svm_cf0_ov_acc<-ad_tda_kde_5.60.5_n1_db_svm_cf0$overall[1]
ad_tda_kde_5.60.5_n1_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8838384 0.9945711 0.9459459 0.9876011 0.9459459
## Class: BOMBAY 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## Class: CALI 0.9447853 0.9905319 0.9314516 0.9924665 0.9314516
## Class: DERMASON 0.7855127 0.9913822 0.9698026 0.9291705 0.9698026
## Class: HOROZ 0.9671280 0.9934323 0.9604811 0.9945683 0.9604811
## Class: SEKER 0.9621711 0.9873272 0.9300477 0.9933353 0.9300477
## Class: SIRA 0.9417722 0.9264438 0.7545639 0.9851325 0.7545639
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8838384 0.9138381 0.09705882 0.08578431
## Class: BOMBAY 1.0000000 1.0000000 0.03823529 0.03823529
## Class: CALI 0.9447853 0.9380711 0.11985294 0.11323529
## Class: DERMASON 0.7855127 0.8679834 0.26053922 0.20465686
## Class: HOROZ 0.9671280 0.9637931 0.14166667 0.13700980
## Class: SEKER 0.9621711 0.9458367 0.14901961 0.14338235
## Class: SIRA 0.9417722 0.8378378 0.19362745 0.18235294
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09068627 0.9392048
## Class: BOMBAY 0.03823529 1.0000000
## Class: CALI 0.12156863 0.9676586
## Class: DERMASON 0.21102941 0.8884474
## Class: HOROZ 0.14264706 0.9802802
## Class: SEKER 0.15416667 0.9747491
## Class: SIRA 0.24166667 0.9341080
ad_tda_kde_5.60.5_n1_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n1_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_svm_n1_3_fold<-(db_svm_fit_re - ad_tda_kde_5.60.5_n1_svm_fit_re)
diff_drybean_tda_kde_5.60.5_svm_n1_3_fold
## Accuracy
## 1 -0.02466977
## 2 -0.02216766
## 3 -0.01810598
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n1_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n1_3_fold_odds.left<-bst_tda_kde_5.60.5_svm.n1_3_fold$probLeft/bst_tda_kde_5.60.5_svm.n1_3_fold$probRight
bst_tda_kde_5.60.5_svm.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n1_3_fold
## $winLeft
## [1] 0.9640333
##
## $winRope
## [1] 0.03596667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n1_3_fold
## $left
## [1] 0.9829405
##
## $rope
## [1] 0.01464241
##
## $right
## [1] 0.002417064
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold))
#bf_tda_kde_5.60.5_svm.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_svm_n1_3_fold)
## t = -11.319, df = 2, p-value = 0.007715
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.02987683 -0.01341877
## sample estimates:
## mean of x
## -0.0216478
### Test set diff
diff_drybean_tda_kde_5.60.5_svm.n1_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.60.5_n1_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_svm.n1_test
## Accuracy
## 0.02279412
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n1_test),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n1_test_odds.left<-bst_tda_kde_5.60.5_svm.n1_test$probLeft/bst_tda_kde_5.60.5_svm.n1_test$probRight
bst_tda_kde_5.60.5_svm.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n1_test),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1624
##
## $winRight
## [1] 0.8376
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_svm.n1_test))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm.n1_test)) #bf_tda_kde_5.60.5_svm.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n1_test))
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node2
DryBean_TDA_KDE_5.60.5_n2_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n2.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_KDE_5.60.5_n2_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 7002 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4668, 4668, 4668
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.9450157 0.9291274
## 0.1 0.50 0.9465867 0.9311982
## 0.1 0.75 0.9467295 0.9314036
## 0.1 1.00 0.9484433 0.9336262
## 0.1 1.25 0.9487289 0.9339972
## 1.0 0.25 0.9324479 0.9130216
## 1.0 0.50 0.9367324 0.9186424
## 1.0 0.75 0.9411597 0.9243486
## 1.0 1.00 0.9420166 0.9254547
## 1.0 1.25 0.9424450 0.9260017
## 10.0 0.25 0.5488432 0.3681545
## 10.0 0.50 0.5971151 0.4404817
## 10.0 0.75 0.6375321 0.4995681
## 10.0 1.00 0.6730934 0.5512097
## 10.0 1.25 0.6918023 0.5786817
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.25.
DryBean_TDA_KDE_5.60.5_n2_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.9532991 0.9399776 Fold1
## 2 0.9481577 0.9332709 Fold3
## 3 0.9447301 0.9287431 Fold2
ad_tda_kde_5.60.5_n2_svm_fit_re<-DryBean_TDA_KDE_5.60.5_n2_SvmFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n2_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_KDE_5.60.5_n2_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n2_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n2_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n2_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n2_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n2_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 312 0 11 0 2 3 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 36 0 453 0 18 0 0
## DERMASON 0 0 0 943 4 8 44
## HOROZ 26 156 14 1 547 0 10
## SEKER 7 0 1 12 0 578 11
## SIRA 15 0 10 107 7 19 722
##
## Overall Statistics
##
## Accuracy : 0.8713
## 95% CI : (0.8607, 0.8815)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8437
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.78788 0.00000 0.9264 0.8871
## Specificity 0.99484 1.00000 0.9850 0.9814
## Pos Pred Value 0.94260 NaN 0.8935 0.9439
## Neg Pred Value 0.97759 0.96176 0.9899 0.9611
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07647 0.00000 0.1110 0.2311
## Detection Prevalence 0.08113 0.00000 0.1243 0.2449
## Balanced Accuracy 0.89136 0.50000 0.9557 0.9343
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9464 0.9507 0.9139
## Specificity 0.9409 0.9911 0.9520
## Pos Pred Value 0.7255 0.9491 0.8205
## Neg Pred Value 0.9907 0.9914 0.9787
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1341 0.1417 0.1770
## Detection Prevalence 0.1848 0.1493 0.2157
## Balanced Accuracy 0.9436 0.9709 0.9329
ad_tda_kde_5.60.5_n2_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 312 0 11 0 2 3 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 36 0 453 0 18 0 0
## DERMASON 0 0 0 943 4 8 44
## HOROZ 26 156 14 1 547 0 10
## SEKER 7 0 1 12 0 578 11
## SIRA 15 0 10 107 7 19 722
##
## Overall Statistics
##
## Accuracy : 0.8713
## 95% CI : (0.8607, 0.8815)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8437
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.78788 0.00000 0.9264 0.8871
## Specificity 0.99484 1.00000 0.9850 0.9814
## Pos Pred Value 0.94260 NaN 0.8935 0.9439
## Neg Pred Value 0.97759 0.96176 0.9899 0.9611
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07647 0.00000 0.1110 0.2311
## Detection Prevalence 0.08113 0.00000 0.1243 0.2449
## Balanced Accuracy 0.89136 0.50000 0.9557 0.9343
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9464 0.9507 0.9139
## Specificity 0.9409 0.9911 0.9520
## Pos Pred Value 0.7255 0.9491 0.8205
## Neg Pred Value 0.9907 0.9914 0.9787
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1341 0.1417 0.1770
## Detection Prevalence 0.1848 0.1493 0.2157
## Balanced Accuracy 0.9436 0.9709 0.9329
ad_tda_kde_5.60.5_n2_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8713235 0.8436973 0.8606602 0.8814519 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
ad_tda_kde_5.60.5_n2_db_svm_cf0_ov_acc<-ad_tda_kde_5.60.5_n2_db_svm_cf0$overall[1]
ad_tda_kde_5.60.5_n2_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.7878788 0.9948426 0.9425982 0.9775940 0.9425982
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9263804 0.9849624 0.8934911 0.9899244 0.8934911
## Class: DERMASON 0.8871119 0.9814385 0.9439439 0.9610516 0.9439439
## Class: HOROZ 0.9463668 0.9408909 0.7254642 0.9906795 0.7254642
## Class: SEKER 0.9506579 0.9910714 0.9490969 0.9913570 0.9490969
## Class: SIRA 0.9139241 0.9519757 0.8204545 0.9787500 0.8204545
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7878788 0.8583219 0.09705882 0.07647059
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.9263804 0.9096386 0.11985294 0.11102941
## Class: DERMASON 0.8871119 0.9146460 0.26053922 0.23112745
## Class: HOROZ 0.9463668 0.8213213 0.14166667 0.13406863
## Class: SEKER 0.9506579 0.9498767 0.14901961 0.14166667
## Class: SIRA 0.9139241 0.8646707 0.19362745 0.17696078
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.08112745 0.8913607
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.12426471 0.9556714
## Class: DERMASON 0.24485294 0.9342752
## Class: HOROZ 0.18480392 0.9436289
## Class: SEKER 0.14926471 0.9708647
## Class: SIRA 0.21568627 0.9329499
ad_tda_kde_5.60.5_n2_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n2_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_svm_n2_3_fold<-(db_svm_fit_re - ad_tda_kde_5.60.5_n2_svm_fit_re)
diff_drybean_tda_kde_5.60.5_svm_n2_3_fold
## Accuracy
## 1 -0.024750112
## 2 -0.018686636
## 3 -0.009236056
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n2_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n2_3_fold_odds.left<-bst_tda_kde_5.60.5_svm.n2_3_fold$probLeft/bst_tda_kde_5.60.5_svm.n2_3_fold$probRight
bst_tda_kde_5.60.5_svm.n2_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n2_3_fold
## $winLeft
## [1] 0.7839667
##
## $winRope
## [1] 0.2160333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n2_3_fold
## $left
## [1] 0.8579384
##
## $rope
## [1] 0.1250804
##
## $right
## [1] 0.01698118
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold))
#bf_tda_kde_5.60.5_svm.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_svm_n2_3_fold)
## t = -3.8896, df = 2, p-value = 0.06019
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.036979607 0.001864404
## sample estimates:
## mean of x
## -0.0175576
### Test set diff
diff_drybean_tda_kde_5.60.5_svm.n2_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.60.5_n2_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_svm.n2_test
## Accuracy
## 0.05612745
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n2_test),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n2_test_odds.left<-bst_tda_kde_5.60.5_svm.n2_test$probLeft/bst_tda_kde_5.60.5_svm.n2_test$probRight
bst_tda_kde_5.60.5_svm.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n2_test),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1562
##
## $winRight
## [1] 0.8438
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_svm.n2_test))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm.n2_test)) #bf_tda_kde_5.60.5_svm.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n2_test))
##Node3
DryBean_TDA_KDE_5.60.5_n3_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n3.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_KDE_5.60.5_n3_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 3511 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 2341, 2340, 2341
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.6068354 0.53500483
## 0.1 0.50 0.6099696 0.54063224
## 0.1 0.75 0.6116790 0.54350956
## 0.1 1.00 0.6116792 0.54372319
## 0.1 1.25 0.6116792 0.54375216
## 1.0 0.25 0.5925885 0.50921985
## 1.0 0.50 0.5982874 0.52007463
## 1.0 0.75 0.5991424 0.52211295
## 1.0 1.00 0.6005669 0.52470216
## 1.0 1.25 0.6008518 0.52515746
## 10.0 0.25 0.3429494 0.00000000
## 10.0 0.50 0.3515013 0.02093933
## 10.0 0.75 0.3791517 0.08792423
## 10.0 1.00 0.4350016 0.20004269
## 10.0 1.25 0.4486819 0.23093335
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.
DryBean_TDA_KDE_5.60.5_n3_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.911111111 0.8650196 Fold1
## 2 0.005977797 -0.1093663 Fold2
## 3 0.917948718 0.8755163 Fold3
ad_tda_kde_5.60.5_n3_svm_fit_re<-DryBean_TDA_KDE_5.60.5_n3_SvmFit0 $resample[1]
summary(DryBean_TDA_KDE_5.60.5_n3_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_KDE_5.60.5_n3_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n3_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n3_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n3_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n3_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n3_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 2 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 377 156 481 1003 272 49 85
## HOROZ 1 0 6 0 294 0 4
## SEKER 1 0 0 11 0 533 6
## SIRA 15 0 2 49 12 26 695
##
## Overall Statistics
##
## Accuracy : 0.6194
## 95% CI : (0.6043, 0.6343)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5099
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.0050505 0.00000 0.0000 0.9436
## Specificity 1.0000000 1.00000 1.0000 0.5293
## Pos Pred Value 1.0000000 NaN NaN 0.4139
## Neg Pred Value 0.9033840 0.96176 0.8801 0.9638
## Prevalence 0.0970588 0.03824 0.1199 0.2605
## Detection Rate 0.0004902 0.00000 0.0000 0.2458
## Detection Prevalence 0.0004902 0.00000 0.0000 0.5939
## Balanced Accuracy 0.5025253 0.50000 0.5000 0.7364
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.50865 0.8766 0.8797
## Specificity 0.99686 0.9948 0.9684
## Pos Pred Value 0.96393 0.9673 0.8698
## Neg Pred Value 0.92477 0.9787 0.9710
## Prevalence 0.14167 0.1490 0.1936
## Detection Rate 0.07206 0.1306 0.1703
## Detection Prevalence 0.07475 0.1350 0.1958
## Balanced Accuracy 0.75275 0.9357 0.9241
ad_tda_kde_5.60.5_n3_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 2 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 377 156 481 1003 272 49 85
## HOROZ 1 0 6 0 294 0 4
## SEKER 1 0 0 11 0 533 6
## SIRA 15 0 2 49 12 26 695
##
## Overall Statistics
##
## Accuracy : 0.6194
## 95% CI : (0.6043, 0.6343)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5099
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.0050505 0.00000 0.0000 0.9436
## Specificity 1.0000000 1.00000 1.0000 0.5293
## Pos Pred Value 1.0000000 NaN NaN 0.4139
## Neg Pred Value 0.9033840 0.96176 0.8801 0.9638
## Prevalence 0.0970588 0.03824 0.1199 0.2605
## Detection Rate 0.0004902 0.00000 0.0000 0.2458
## Detection Prevalence 0.0004902 0.00000 0.0000 0.5939
## Balanced Accuracy 0.5025253 0.50000 0.5000 0.7364
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.50865 0.8766 0.8797
## Specificity 0.99686 0.9948 0.9684
## Pos Pred Value 0.96393 0.9673 0.8698
## Neg Pred Value 0.92477 0.9787 0.9710
## Prevalence 0.14167 0.1490 0.1936
## Detection Rate 0.07206 0.1306 0.1703
## Detection Prevalence 0.07475 0.1350 0.1958
## Balanced Accuracy 0.75275 0.9357 0.9241
ad_tda_kde_5.60.5_n3_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.6193627 0.5098616 0.6042604 0.6342940 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
ad_tda_kde_5.60.5_n3_db_svm_cf0_ov_acc<-ad_tda_kde_5.60.5_n3_db_svm_cf0$overall[1]
ad_tda_kde_5.60.5_n3_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.005050505 1.0000000 1.0000000 0.9033840 1.0000000
## Class: BOMBAY 0.000000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.000000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.943555974 0.5293338 0.4139496 0.9637900 0.4139496
## Class: HOROZ 0.508650519 0.9968589 0.9639344 0.9247682 0.9639344
## Class: SEKER 0.876644737 0.9948157 0.9673321 0.9787475 0.9673321
## Class: SIRA 0.879746835 0.9683891 0.8698373 0.9710454 0.8698373
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.005050505 0.01005025 0.09705882 0.0004901961
## Class: BOMBAY 0.000000000 NA 0.03823529 0.0000000000
## Class: CALI 0.000000000 NA 0.11985294 0.0000000000
## Class: DERMASON 0.943555974 0.57544464 0.26053922 0.2458333333
## Class: HOROZ 0.508650519 0.66591166 0.14166667 0.0720588235
## Class: SEKER 0.876644737 0.91975841 0.14901961 0.1306372549
## Class: SIRA 0.879746835 0.87476400 0.19362745 0.1703431373
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0004901961 0.5025253
## Class: BOMBAY 0.0000000000 0.5000000
## Class: CALI 0.0000000000 0.5000000
## Class: DERMASON 0.5938725490 0.7364449
## Class: HOROZ 0.0747549020 0.7527547
## Class: SEKER 0.1350490196 0.9357302
## Class: SIRA 0.1958333333 0.9240679
ad_tda_kde_5.60.5_n3_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n3_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_svm_n3_3_fold<-(db_svm_fit_re - ad_tda_kde_5.60.5_n3_svm_fit_re)
diff_drybean_tda_kde_5.60.5_svm_n3_3_fold
## Accuracy
## 1 0.01743783
## 2 0.92349324
## 3 0.01754530
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n3_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n3_3_fold_odds.left<-bst_tda_kde_5.60.5_svm.n3_3_fold$probLeft/bst_tda_kde_5.60.5_svm.n3_3_fold$probRight
bst_tda_kde_5.60.5_svm.n3_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n3_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.09296667
##
## $winRight
## [1] 0.9070333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n3_3_fold
## $left
## [1] 0.2222324
##
## $rope
## [1] 0.01198793
##
## $right
## [1] 0.7657796
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold))
#bf_tda_kde_5.60.5_svm.n3_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_svm_n3_3_fold)
## t = 1.0579, df = 2, p-value = 0.401
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9799114 1.6188956
## sample estimates:
## mean of x
## 0.3194921
### Test set diff
diff_drybean_tda_kde_5.60.5_svm.n3_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.60.5_n3_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_svm.n3_test
## Accuracy
## 0.3080882
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n3_test),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n3_test_odds.left<-bst_tda_kde_5.60.5_svm.n3_test$probLeft/bst_tda_kde_5.60.5_svm.n3_test$probRight
bst_tda_kde_5.60.5_svm.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n3_test),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n3_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1594333
##
## $winRight
## [1] 0.8405667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_svm.n3_test))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm.n3_test)) #bf_tda_kde_5.60.5_svm.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n3_test))
##Node4
DryBean_TDA_KDE_5.60.5_n4_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n4.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_KDE_5.60.5_n4_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 1759 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 1173, 1173, 1172
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.8158130 0.680258892
## 0.1 0.50 0.8180864 0.685876855
## 0.1 0.75 0.8158111 0.683351373
## 0.1 1.00 0.8152452 0.683724462
## 0.1 1.25 0.8124039 0.679251901
## 1.0 0.25 0.7345065 0.494820303
## 1.0 0.50 0.7652077 0.568169352
## 1.0 0.75 0.7822677 0.607898684
## 1.0 1.00 0.7913641 0.630768048
## 1.0 1.25 0.7964758 0.643254570
## 10.0 0.25 0.5321209 0.000000000
## 10.0 0.50 0.5321209 0.000000000
## 10.0 0.75 0.5326897 0.001538908
## 10.0 1.00 0.5378062 0.015322974
## 10.0 1.25 0.5491741 0.046353028
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 0.5.
DryBean_TDA_KDE_5.60.5_n4_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.8040886 0.6641509 Fold3
## 2 0.8378840 0.7190484 Fold1
## 3 0.8122867 0.6744313 Fold2
ad_tda_kde_5.60.5_n4_svm_fit_re<-DryBean_TDA_KDE_5.60.5_n4_SvmFit0 $resample[1]
summary(DryBean_TDA_KDE_5.60.5_n4_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_KDE_5.60.5_n4_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n4_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n4_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n4_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n4_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 395 156 489 1024 578 228 580
## HOROZ 0 0 0 0 0 0 0
## SEKER 0 0 0 8 0 374 4
## SIRA 1 0 0 31 0 6 206
##
## Overall Statistics
##
## Accuracy : 0.3931
## 95% CI : (0.3781, 0.4083)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.1952
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9633
## Specificity 1.00000 1.00000 1.0000 0.1959
## Pos Pred Value NaN NaN NaN 0.2968
## Neg Pred Value 0.90294 0.96176 0.8801 0.9381
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2510
## Detection Prevalence 0.00000 0.00000 0.0000 0.8456
## Balanced Accuracy 0.50000 0.50000 0.5000 0.5796
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.61513 0.26076
## Specificity 1.0000 0.99654 0.98845
## Pos Pred Value NaN 0.96891 0.84426
## Neg Pred Value 0.8583 0.93665 0.84776
## Prevalence 0.1417 0.14902 0.19363
## Detection Rate 0.0000 0.09167 0.05049
## Detection Prevalence 0.0000 0.09461 0.05980
## Balanced Accuracy 0.5000 0.80584 0.62460
ad_tda_kde_5.60.5_n4_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 395 156 489 1024 578 228 580
## HOROZ 0 0 0 0 0 0 0
## SEKER 0 0 0 8 0 374 4
## SIRA 1 0 0 31 0 6 206
##
## Overall Statistics
##
## Accuracy : 0.3931
## 95% CI : (0.3781, 0.4083)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.1952
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9633
## Specificity 1.00000 1.00000 1.0000 0.1959
## Pos Pred Value NaN NaN NaN 0.2968
## Neg Pred Value 0.90294 0.96176 0.8801 0.9381
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2510
## Detection Prevalence 0.00000 0.00000 0.0000 0.8456
## Balanced Accuracy 0.50000 0.50000 0.5000 0.5796
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.61513 0.26076
## Specificity 1.0000 0.99654 0.98845
## Pos Pred Value NaN 0.96891 0.84426
## Neg Pred Value 0.8583 0.93665 0.84776
## Prevalence 0.1417 0.14902 0.19363
## Detection Rate 0.0000 0.09167 0.05049
## Detection Prevalence 0.0000 0.09461 0.05980
## Balanced Accuracy 0.5000 0.80584 0.62460
ad_tda_kde_5.60.5_n4_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.931373e-01 1.951561e-01 3.781080e-01 4.083197e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 2.291637e-76 NaN
ad_tda_kde_5.60.5_n4_db_svm_cf0_ov_acc<-ad_tda_kde_5.60.5_n4_db_svm_cf0$overall[1]
ad_tda_kde_5.60.5_n4_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9633114 0.1958900 0.2968116 0.9380952 0.2968116
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.6151316 0.9965438 0.9689119 0.9366540 0.9689119
## Class: SIRA 0.2607595 0.9884498 0.8442623 0.8477581 0.8442623
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.00000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.0000000 NA 0.11985294 0.00000000
## Class: DERMASON 0.9633114 0.4538001 0.26053922 0.25098039
## Class: HOROZ 0.0000000 NA 0.14166667 0.00000000
## Class: SEKER 0.6151316 0.7525151 0.14901961 0.09166667
## Class: SIRA 0.2607595 0.3984526 0.19362745 0.05049020
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.00000000 0.5000000
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.00000000 0.5000000
## Class: DERMASON 0.84558824 0.5796007
## Class: HOROZ 0.00000000 0.5000000
## Class: SEKER 0.09460784 0.8058377
## Class: SIRA 0.05980392 0.6246047
ad_tda_kde_5.60.5_n4_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n4_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_svm_n4_3_fold<-(db_svm_fit_re - ad_tda_kde_5.60.5_n4_svm_fit_re)
diff_drybean_tda_kde_5.60.5_svm_n4_3_fold
## Accuracy
## 1 0.12446036
## 2 0.09158707
## 3 0.12320733
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n4_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n4_3_fold_odds.left<-bst_tda_kde_5.60.5_svm.n4_3_fold$probLeft/bst_tda_kde_5.60.5_svm.n4_3_fold$probRight
bst_tda_kde_5.60.5_svm.n4_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n4_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0097
##
## $winRight
## [1] 0.9903
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n4_3_fold
## $left
## [1] 0.005013603
##
## $rope
## [1] 0.002088858
##
## $right
## [1] 0.9928975
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold))
#bf_tda_kde_5.60.5_svm.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_svm_n4_3_fold)
## t = 10.515, df = 2, p-value = 0.008924
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.06680986 0.15935999
## sample estimates:
## mean of x
## 0.1130849
### Test set diff
diff_drybean_tda_kde_5.60.5_svm.n4_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.60.5_n4_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_svm.n4_test
## Accuracy
## 0.5343137
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n4_test),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n4_test_odds.left<-bst_tda_kde_5.60.5_svm.n4_test$probLeft/bst_tda_kde_5.60.5_svm.n4_test$probRight
bst_tda_kde_5.60.5_svm.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n4_test),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1624333
##
## $winRight
## [1] 0.8375667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_svm.n4_test))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm.n4_test)) #bf_tda_kde_5.60.5_svm.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n4_test))
##Node5
DryBean_TDA_KDE_5.60.5_n5_SvmFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n5.vec,
Importance = T,
method = 'svmRadial',
trControl = fitControl,
tuneGrid = svmGrid, preProc = c('center','scale'),
metric='Accuracy')
DryBean_TDA_KDE_5.60.5_n5_SvmFit0
## Support Vector Machines with Radial Basis Function Kernel
##
## 774 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## Pre-processing: centered (16), scaled (16)
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 516, 515, 517
## Resampling results across tuning parameters:
##
## sigma C Accuracy Kappa
## 0.1 0.25 0.6460420 0.33122384
## 0.1 0.50 0.6473090 0.34840774
## 0.1 0.75 0.6486010 0.35951461
## 0.1 1.00 0.6499031 0.36756982
## 0.1 1.25 0.6447350 0.36354658
## 1.0 0.25 0.5788022 0.05426709
## 1.0 0.50 0.6137065 0.18698345
## 1.0 0.75 0.6136765 0.21415995
## 1.0 1.00 0.6175626 0.24020761
## 1.0 1.25 0.6304977 0.28880207
## 10.0 0.25 0.5658971 0.00000000
## 10.0 0.50 0.5658971 0.00000000
## 10.0 0.75 0.5658971 0.00000000
## 10.0 1.00 0.5658971 0.00000000
## 10.0 1.25 0.5658971 0.00000000
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.1 and C = 1.
DryBean_TDA_KDE_5.60.5_n5_SvmFit0$resample
## Accuracy Kappa Resample
## 1 0.5038760 0.1462475 Fold1
## 2 0.7104247 0.4471954 Fold2
## 3 0.7354086 0.5092665 Fold3
ad_tda_kde_5.60.5_n5_svm_fit_re<-DryBean_TDA_KDE_5.60.5_n5_SvmFit0 $resample[1]
summary(DryBean_TDA_KDE_5.60.5_n5_SvmFit0)
## Length Class Mode
## 1 ksvm S4
#vip(DryBean_TDA_KDE_5.60.5_n5_SvmFit0,25) + ggtitle("DryBean_TDA_KDE_5.60.5_n5_SvmFit TDA-Assited Svm")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n5_SvmFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n5_SvmFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
ad_tda_kde_5.60.5_n5_db_svm_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
ad_tda_kde_5.60.5_n5_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 396 156 489 1049 578 458 756
## HOROZ 0 0 0 0 0 0 0
## SEKER 0 0 0 7 0 146 3
## SIRA 0 0 0 7 0 4 31
##
## Overall Statistics
##
## Accuracy : 0.3005
## 95% CI : (0.2864, 0.3148)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 5.635e-09
##
## Kappa : 0.0603
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.98683
## Specificity 1.00000 1.00000 1.0000 0.06099
## Pos Pred Value NaN NaN NaN 0.27022
## Neg Pred Value 0.90294 0.96176 0.8801 0.92929
## Prevalence 0.09706 0.03824 0.1199 0.26054
## Detection Rate 0.00000 0.00000 0.0000 0.25711
## Detection Prevalence 0.00000 0.00000 0.0000 0.95147
## Balanced Accuracy 0.50000 0.50000 0.5000 0.52391
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.24013 0.039241
## Specificity 1.0000 0.99712 0.996657
## Pos Pred Value NaN 0.93590 0.738095
## Neg Pred Value 0.8583 0.88226 0.812036
## Prevalence 0.1417 0.14902 0.193627
## Detection Rate 0.0000 0.03578 0.007598
## Detection Prevalence 0.0000 0.03824 0.010294
## Balanced Accuracy 0.5000 0.61863 0.517949
ad_tda_kde_5.60.5_n5_db_svm_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 396 156 489 1049 578 458 756
## HOROZ 0 0 0 0 0 0 0
## SEKER 0 0 0 7 0 146 3
## SIRA 0 0 0 7 0 4 31
##
## Overall Statistics
##
## Accuracy : 0.3005
## 95% CI : (0.2864, 0.3148)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 5.635e-09
##
## Kappa : 0.0603
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.98683
## Specificity 1.00000 1.00000 1.0000 0.06099
## Pos Pred Value NaN NaN NaN 0.27022
## Neg Pred Value 0.90294 0.96176 0.8801 0.92929
## Prevalence 0.09706 0.03824 0.1199 0.26054
## Detection Rate 0.00000 0.00000 0.0000 0.25711
## Detection Prevalence 0.00000 0.00000 0.0000 0.95147
## Balanced Accuracy 0.50000 0.50000 0.5000 0.52391
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.24013 0.039241
## Specificity 1.0000 0.99712 0.996657
## Pos Pred Value NaN 0.93590 0.738095
## Neg Pred Value 0.8583 0.88226 0.812036
## Prevalence 0.1417 0.14902 0.193627
## Detection Rate 0.0000 0.03578 0.007598
## Detection Prevalence 0.0000 0.03824 0.010294
## Balanced Accuracy 0.5000 0.61863 0.517949
ad_tda_kde_5.60.5_n5_db_svm_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.004902e-01 6.032099e-02 2.864467e-01 3.148199e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 5.635067e-09 NaN
ad_tda_kde_5.60.5_n5_db_svm_cf0_ov_acc<-ad_tda_kde_5.60.5_n5_db_svm_cf0$overall[1]
ad_tda_kde_5.60.5_n5_db_svm_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.00000000 1.00000000 NaN 0.9029412 NA
## Class: BOMBAY 0.00000000 1.00000000 NaN 0.9617647 NA
## Class: CALI 0.00000000 1.00000000 NaN 0.8801471 NA
## Class: DERMASON 0.98682973 0.06098774 0.2702215 0.9292929 0.2702215
## Class: HOROZ 0.00000000 1.00000000 NaN 0.8583333 NA
## Class: SEKER 0.24013158 0.99711982 0.9358974 0.8822630 0.9358974
## Class: SIRA 0.03924051 0.99665653 0.7380952 0.8120357 0.7380952
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.00000000 NA 0.09705882 0.000000000
## Class: BOMBAY 0.00000000 NA 0.03823529 0.000000000
## Class: CALI 0.00000000 NA 0.11985294 0.000000000
## Class: DERMASON 0.98682973 0.42426694 0.26053922 0.257107843
## Class: HOROZ 0.00000000 NA 0.14166667 0.000000000
## Class: SEKER 0.24013158 0.38219895 0.14901961 0.035784314
## Class: SIRA 0.03924051 0.07451923 0.19362745 0.007598039
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.00000000 0.5000000
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.00000000 0.5000000
## Class: DERMASON 0.95147059 0.5239087
## Class: HOROZ 0.00000000 0.5000000
## Class: SEKER 0.03823529 0.6186257
## Class: SIRA 0.01029412 0.5179485
ad_tda_kde_5.60.5_n5_db_svm_cf0_pre_rec_f1<-ad_tda_kde_5.60.5_n5_db_svm_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_svm_n5_3_fold<-(db_svm_fit_re - ad_tda_kde_5.60.5_n5_svm_fit_re)
diff_drybean_tda_kde_5.60.5_svm_n5_3_fold
## Accuracy
## 1 0.4246730
## 2 0.2190463
## 3 0.2000855
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n5_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n5_3_fold_odds.left<-bst_tda_kde_5.60.5_svm.n5_3_fold$probLeft/bst_tda_kde_5.60.5_svm.n5_3_fold$probRight
bst_tda_kde_5.60.5_svm.n5_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n5_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008633333
##
## $winRight
## [1] 0.9913667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n5_3_fold
## $left
## [1] 0.03627002
##
## $rope
## [1] 0.00487849
##
## $right
## [1] 0.9588515
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold))
#bf_tda_kde_5.60.5_svm.n5_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_svm_n5_3_fold)
## t = 3.9113, df = 2, p-value = 0.05958
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.0281397 0.5906762
## sample estimates:
## mean of x
## 0.2812683
### Test set diff
diff_drybean_tda_kde_5.60.5_svm.n5_test<-(db_svm_cf_ov_acc-ad_tda_kde_5.60.5_n5_db_svm_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_svm.n5_test
## Accuracy
## 0.6269608
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n5_test),-0.01,0.01)
bst_tda_kde_5.60.5_svm.n5_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_svm.n5_test_odds.left<-bst_tda_kde_5.60.5_svm.n5_test$probLeft/bst_tda_kde_5.60.5_svm.n5_test$probRight
bst_tda_kde_5.60.5_svm.n5_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_svm.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n5_test),-0.01,0.01)
bsr_tda_kde_5.60.5_svm.n5_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1574333
##
## $winRight
## [1] 0.8425667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_svm.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_svm.n5_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_svm.n5_test))
#BayesFactor
#bf_tda_kde_5.60.5_svm.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_svm.n5_test)) #bf_tda_kde_5.60.5_svm.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_svm.n5_test))
#Non-TDA-Assisted
nn1Grid<-expand.grid(size = c(2,3,5,7), decay = c(0.3,0.5,0.7))
#Neural Network
dryBeanNn1Fit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 55
## initial value 14210.828654
## iter 10 value 11687.640603
## iter 20 value 11657.732161
## iter 30 value 11646.317974
## iter 40 value 11375.572009
## iter 50 value 10186.662906
## iter 60 value 9559.659419
## iter 70 value 9012.444143
## iter 80 value 7799.703709
## iter 90 value 7136.826987
## iter 100 value 6579.957270
## final value 6579.957270
## stopped after 100 iterations
## # weights: 79
## initial value 15119.880886
## iter 10 value 11657.216869
## final value 11657.198644
## converged
## # weights: 127
## initial value 14711.069274
## iter 10 value 11657.197444
## iter 20 value 11015.686697
## iter 30 value 10714.220673
## iter 40 value 9397.513400
## iter 50 value 9229.604739
## iter 60 value 8293.950931
## iter 70 value 7620.145089
## iter 80 value 6747.502403
## iter 90 value 6634.694108
## iter 100 value 6427.466188
## final value 6427.466188
## stopped after 100 iterations
## # weights: 175
## initial value 13448.798183
## iter 10 value 11657.211009
## final value 11657.198756
## converged
## # weights: 55
## initial value 13149.943071
## iter 10 value 11662.165291
## iter 20 value 11639.003795
## iter 30 value 10675.130086
## iter 40 value 9099.870910
## iter 50 value 8706.325979
## iter 60 value 8603.323444
## iter 70 value 8444.787267
## iter 80 value 7429.823629
## iter 90 value 7000.604721
## iter 100 value 6662.745633
## final value 6662.745633
## stopped after 100 iterations
## # weights: 79
## initial value 12768.865642
## iter 10 value 11659.683150
## iter 20 value 11657.325520
## iter 30 value 11214.042394
## iter 40 value 10248.338936
## iter 50 value 9795.233799
## iter 60 value 9511.806399
## iter 70 value 9256.215266
## iter 80 value 8962.606629
## iter 90 value 8716.317545
## iter 100 value 8582.496118
## final value 8582.496118
## stopped after 100 iterations
## # weights: 127
## initial value 19663.921409
## iter 10 value 12191.612110
## iter 20 value 11747.319246
## iter 30 value 11657.532134
## iter 40 value 11522.483498
## iter 50 value 10525.499094
## iter 60 value 10248.441360
## iter 70 value 9908.262706
## iter 80 value 9223.496229
## iter 90 value 8041.905688
## iter 100 value 6678.104238
## final value 6678.104238
## stopped after 100 iterations
## # weights: 175
## initial value 12571.831542
## iter 10 value 11683.360538
## iter 20 value 11657.532794
## iter 30 value 11657.257618
## final value 11657.254601
## converged
## # weights: 55
## initial value 12725.444452
## iter 10 value 11812.369849
## iter 20 value 11658.053268
## iter 30 value 10245.993778
## iter 40 value 10057.173475
## iter 50 value 9864.057206
## iter 60 value 9798.228104
## iter 70 value 8964.128750
## iter 80 value 8626.932436
## iter 90 value 8478.135312
## iter 100 value 8315.634962
## final value 8315.634962
## stopped after 100 iterations
## # weights: 79
## initial value 15717.785001
## iter 10 value 11657.817932
## final value 11657.366098
## converged
## # weights: 127
## initial value 14150.808234
## iter 10 value 11658.141192
## final value 11657.496153
## converged
## # weights: 175
## initial value 13159.042532
## iter 10 value 11710.785339
## iter 20 value 11657.673193
## iter 30 value 11621.113149
## iter 40 value 11565.965473
## iter 50 value 11429.938966
## iter 60 value 8335.494823
## iter 70 value 7850.002568
## iter 80 value 7334.367154
## iter 90 value 6383.516023
## iter 100 value 5605.559707
## final value 5605.559707
## stopped after 100 iterations
## # weights: 55
## initial value 11946.764974
## iter 10 value 11657.441061
## final value 11657.436618
## converged
## # weights: 79
## initial value 12445.546762
## iter 10 value 11658.398912
## iter 20 value 11538.715278
## iter 30 value 10893.067021
## iter 40 value 10704.789587
## iter 50 value 10668.262528
## iter 60 value 10666.029164
## iter 70 value 10665.257537
## final value 10665.227772
## converged
## # weights: 127
## initial value 14120.483380
## iter 10 value 11697.667920
## iter 20 value 11620.604810
## iter 30 value 10108.092906
## iter 40 value 9253.154893
## iter 50 value 9079.651804
## iter 60 value 7861.564426
## iter 70 value 7562.803942
## iter 80 value 7415.785398
## iter 90 value 7353.316344
## iter 100 value 7206.350346
## final value 7206.350346
## stopped after 100 iterations
## # weights: 175
## initial value 12563.178623
## iter 10 value 11659.273994
## iter 20 value 11637.226252
## iter 30 value 11150.002507
## iter 40 value 10537.569723
## iter 50 value 9015.373210
## iter 60 value 7863.290313
## iter 70 value 7167.843850
## iter 80 value 6874.408296
## iter 90 value 6817.225668
## iter 100 value 6732.355579
## final value 6732.355579
## stopped after 100 iterations
## # weights: 55
## initial value 14250.998247
## iter 10 value 11700.821122
## iter 20 value 11657.997308
## iter 30 value 11651.614706
## iter 40 value 10996.562097
## iter 50 value 9867.221575
## iter 60 value 9253.853948
## iter 70 value 8848.809990
## iter 80 value 8409.357836
## iter 90 value 8079.778713
## iter 100 value 7902.659632
## final value 7902.659632
## stopped after 100 iterations
## # weights: 79
## initial value 13000.533718
## iter 10 value 11664.099276
## iter 20 value 11657.227152
## iter 30 value 11657.140248
## final value 11657.139336
## converged
## # weights: 127
## initial value 12800.922318
## iter 10 value 11659.262242
## iter 20 value 11657.163839
## final value 11657.139289
## converged
## # weights: 175
## initial value 13029.270994
## iter 10 value 11658.542353
## iter 20 value 11654.646638
## iter 30 value 9759.245025
## iter 40 value 9499.281228
## iter 50 value 9415.893963
## iter 60 value 9398.204523
## iter 70 value 9159.923070
## iter 80 value 9038.052893
## iter 90 value 8961.231727
## iter 100 value 8791.871675
## final value 8791.871675
## stopped after 100 iterations
## # weights: 55
## initial value 13604.121340
## iter 10 value 11658.997570
## iter 20 value 11421.663361
## iter 30 value 10957.611692
## iter 40 value 10790.460139
## iter 50 value 10785.965210
## iter 60 value 10785.257780
## iter 70 value 10778.244866
## iter 80 value 10692.679453
## iter 90 value 9996.846365
## iter 100 value 8490.319966
## final value 8490.319966
## stopped after 100 iterations
## # weights: 79
## initial value 13083.923193
## iter 10 value 11657.454954
## iter 20 value 11577.567657
## iter 30 value 11457.258722
## iter 40 value 9484.689539
## iter 50 value 8870.928455
## iter 60 value 8703.472637
## iter 70 value 7930.617282
## iter 80 value 7180.805614
## iter 90 value 6887.983113
## iter 100 value 6672.383509
## final value 6672.383509
## stopped after 100 iterations
## # weights: 127
## initial value 12876.807894
## iter 10 value 11657.313520
## iter 20 value 11657.288254
## iter 20 value 11657.288216
## final value 11657.287962
## converged
## # weights: 175
## initial value 15234.643067
## iter 10 value 11741.631888
## iter 20 value 11657.079024
## iter 30 value 11644.451583
## iter 40 value 11617.131559
## iter 50 value 10333.101258
## iter 60 value 9603.035273
## iter 70 value 9400.753319
## iter 80 value 9165.383548
## iter 90 value 8692.277556
## iter 100 value 8227.296155
## final value 8227.296155
## stopped after 100 iterations
## # weights: 55
## initial value 12470.157876
## iter 10 value 11656.827505
## final value 11656.805402
## converged
## # weights: 79
## initial value 14684.828806
## iter 10 value 11660.194728
## iter 20 value 11582.493109
## iter 30 value 9437.949387
## iter 40 value 9020.819722
## iter 50 value 8983.275394
## iter 60 value 8734.339963
## iter 70 value 6652.088743
## iter 80 value 5821.061365
## iter 90 value 5503.667722
## iter 100 value 5316.924556
## final value 5316.924556
## stopped after 100 iterations
## # weights: 127
## initial value 16443.484429
## iter 10 value 11858.832414
## iter 20 value 11662.990931
## iter 30 value 11662.950953
## iter 40 value 11657.149598
## iter 40 value 11657.149553
## iter 50 value 11656.806923
## final value 11656.805157
## converged
## # weights: 175
## initial value 13646.883277
## iter 10 value 11656.626815
## final value 11656.604473
## converged
## # weights: 55
## initial value 12583.781733
## iter 10 value 11657.602742
## iter 20 value 11656.992843
## final value 11656.842945
## converged
## # weights: 79
## initial value 12788.438246
## iter 10 value 11687.084380
## iter 20 value 11657.396732
## iter 30 value 10360.547562
## iter 40 value 9249.882658
## iter 50 value 8940.459352
## iter 60 value 8813.841160
## iter 70 value 8778.913337
## iter 80 value 8559.526695
## iter 90 value 8401.205497
## iter 100 value 8147.647296
## final value 8147.647296
## stopped after 100 iterations
## # weights: 127
## initial value 14595.531832
## iter 10 value 11697.530886
## iter 20 value 11657.183503
## iter 30 value 11656.775113
## iter 40 value 9999.131082
## iter 50 value 9079.168972
## iter 60 value 8816.704741
## iter 70 value 8393.179783
## iter 80 value 7919.892315
## iter 90 value 6967.004244
## iter 100 value 6785.921243
## final value 6785.921243
## stopped after 100 iterations
## # weights: 175
## initial value 12669.518063
## iter 10 value 11674.246441
## iter 20 value 11657.090914
## iter 30 value 11656.845520
## iter 40 value 11656.816149
## iter 50 value 11627.476404
## iter 60 value 11013.758170
## iter 70 value 10836.109986
## iter 80 value 10446.576824
## iter 90 value 8670.551660
## iter 100 value 6911.262290
## final value 6911.262290
## stopped after 100 iterations
## # weights: 55
## initial value 14375.341551
## iter 10 value 11659.363865
## final value 11657.251181
## converged
## # weights: 79
## initial value 12718.331545
## iter 10 value 11657.648444
## final value 11656.991331
## converged
## # weights: 127
## initial value 13883.126858
## iter 10 value 11657.073575
## iter 20 value 11618.488665
## iter 30 value 10668.903831
## iter 40 value 10101.044447
## iter 50 value 9602.824120
## iter 60 value 9184.929871
## iter 70 value 8877.991645
## iter 80 value 8786.815337
## iter 90 value 8698.249804
## iter 100 value 8507.447843
## final value 8507.447843
## stopped after 100 iterations
## # weights: 175
## initial value 13801.812567
## iter 10 value 11657.706616
## final value 11656.731543
## converged
## # weights: 175
## initial value 21531.146687
## iter 10 value 17483.938823
## iter 20 value 17440.993898
## iter 30 value 17427.654762
## iter 40 value 16559.153582
## iter 50 value 16151.485652
## iter 60 value 14348.270303
## iter 70 value 13686.442709
## iter 80 value 13621.214815
## iter 90 value 13534.089337
## iter 100 value 13139.638974
## final value 13139.638974
## stopped after 100 iterations
dryBeanNn1Fit
## Neural Network
##
## 9531 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 6354, 6354, 6354
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.3887315 0.18707039
## 2 0.5 0.3807575 0.20300069
## 2 0.7 0.3593537 0.16482011
## 3 0.3 0.4001679 0.20874650
## 3 0.5 0.3500157 0.15375512
## 3 0.7 0.3351170 0.11666548
## 5 0.3 0.4382541 0.27319919
## 5 0.5 0.4612318 0.31211609
## 5 0.7 0.3055293 0.07662268
## 7 0.3 0.3483370 0.13751039
## 7 0.5 0.3978596 0.21418166
## 7 0.7 0.4913440 0.34226942
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
dryBeanNn1Fit$resample
## Accuracy Kappa Resample
## 1 0.4743469 0.3466286 Fold2
## 2 0.7393768 0.6801796 Fold1
## 3 0.2603085 0.0000000 Fold3
db_nn1_fit_re<-dryBeanNn1Fit$resample[1]
summary(dryBeanNn1Fit)
## a 16-7-7 network with 175 weights
## options were - softmax modelling decay=0.7
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.00 0.10 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.00 0.11 0.00 0.00 0.00 0.00 0.00 0.11 0.00 0.00
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4 i9->h4
## 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5 i9->h5
## 0.10 0.27 1.76 2.80 2.29 0.16 -0.02 -0.33 0.59 -0.08
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5
## 0.10 0.09 0.10 0.00 0.00 0.12 0.10
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6 i9->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7 i9->h7
## 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1
## 0.37 0.37 0.37 0.18 0.03 -3.30 0.37 0.37
## b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2
## 0.19 0.18 0.19 -0.04 0.17 -4.76 0.19 0.19
## b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3
## 0.43 0.43 0.43 -0.02 0.07 -4.18 0.43 0.43
## b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4
## -0.20 -0.20 -0.20 -0.07 -0.14 3.31 -0.20 -0.20
## b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5
## 0.12 0.13 0.12 -0.06 0.03 0.72 0.12 0.12
## b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6
## -0.64 -0.65 -0.64 0.07 -0.05 4.93 -0.64 -0.64
## b->o7 h1->o7 h2->o7 h3->o7 h4->o7 h5->o7 h6->o7 h7->o7
## -0.26 -0.26 -0.26 -0.05 -0.11 3.27 -0.26 -0.26
#vip(dryBeanNn1Fit,25) + ggtitle("non-TDA-Assited NN")
# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanNn1Fit, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_nn1_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_nn1_cf
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 363 156 479 0 124 5 7
## DERMASON 33 0 9 1063 454 603 783
## HOROZ 0 0 1 0 0 0 0
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3779
## 95% CI : (0.363, 0.393)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.201
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.9796 1.0000
## Specificity 1.00000 1.00000 0.8176 0.3762
## Pos Pred Value NaN NaN 0.4224 0.3610
## Neg Pred Value 0.90294 0.96176 0.9966 1.0000
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.1174 0.2605
## Detection Prevalence 0.00000 0.00000 0.2779 0.7218
## Balanced Accuracy 0.50000 0.50000 0.8986 0.6881
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000000 0.000 0.0000
## Specificity 0.9997144 1.000 1.0000
## Pos Pred Value 0.0000000 NaN NaN
## Neg Pred Value 0.8582986 0.851 0.8064
## Prevalence 0.1416667 0.149 0.1936
## Detection Rate 0.0000000 0.000 0.0000
## Detection Prevalence 0.0002451 0.000 0.0000
## Balanced Accuracy 0.4998572 0.500 0.5000
db_nn1_cf$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.779412e-01 2.010469e-01 3.630324e-01 3.930249e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 8.628767e-61 NaN
db_nn1_cf_ov_acc<-db_nn1_cf$overall[1]
db_nn1_cf$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9795501 0.8175996 0.4223986 0.9966056 0.4223986
## Class: DERMASON 1.0000000 0.3762015 0.3609508 1.0000000 0.3609508
## Class: HOROZ 0.0000000 0.9997144 0.0000000 0.8582986 0.0000000
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.0000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.0000000
## Class: CALI 0.9795501 0.5902649 0.11985294 0.1174020
## Class: DERMASON 1.0000000 0.5304391 0.26053922 0.2605392
## Class: HOROZ 0.0000000 NaN 0.14166667 0.0000000
## Class: SEKER 0.0000000 NA 0.14901961 0.0000000
## Class: SIRA 0.0000000 NA 0.19362745 0.0000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.000000000 0.5000000
## Class: BOMBAY 0.000000000 0.5000000
## Class: CALI 0.277941176 0.8985748
## Class: DERMASON 0.721813725 0.6881008
## Class: HOROZ 0.000245098 0.4998572
## Class: SEKER 0.000000000 0.5000000
## Class: SIRA 0.000000000 0.5000000
db_nn1_cf_pre_rec_f1<-db_nn1_cf$byClass[5:7]
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node1
#Neural Network 1
DryBean_TDA_PC_5.60.5_n1_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n1.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 52
## initial value 10481.590998
## iter 10 value 4868.892369
## iter 20 value 4757.544783
## iter 30 value 4747.910703
## iter 40 value 4747.472502
## iter 50 value 4747.370322
## final value 4747.367190
## converged
## # weights: 75
## initial value 7552.442234
## iter 10 value 4775.490567
## iter 20 value 4755.116939
## iter 30 value 4754.233899
## iter 40 value 4093.499824
## iter 50 value 3084.223614
## iter 60 value 2963.190982
## iter 70 value 2811.743319
## iter 80 value 2117.494541
## iter 90 value 1549.203824
## iter 100 value 1456.169777
## final value 1456.169777
## stopped after 100 iterations
## # weights: 121
## initial value 10927.288974
## iter 10 value 4935.624860
## iter 20 value 4766.730109
## iter 30 value 4748.411063
## iter 40 value 4300.483608
## iter 50 value 3324.802641
## iter 60 value 2657.035603
## iter 70 value 2567.809420
## iter 80 value 2505.076530
## iter 90 value 2374.275292
## iter 100 value 2346.867240
## final value 2346.867240
## stopped after 100 iterations
## # weights: 167
## initial value 10068.943367
## iter 10 value 4895.720822
## iter 20 value 4311.781896
## iter 30 value 4005.335733
## iter 40 value 3972.027375
## iter 50 value 3526.192050
## iter 60 value 3340.972388
## iter 70 value 3099.746804
## iter 80 value 2615.419915
## iter 90 value 2305.145454
## iter 100 value 2170.297178
## final value 2170.297178
## stopped after 100 iterations
## # weights: 52
## initial value 7306.851174
## iter 10 value 4772.880097
## iter 20 value 4759.346485
## iter 30 value 4751.754890
## iter 40 value 4750.646855
## iter 50 value 4750.539098
## iter 60 value 4750.497703
## iter 70 value 3680.506704
## iter 80 value 2976.373987
## iter 90 value 2725.349547
## iter 100 value 2637.039164
## final value 2637.039164
## stopped after 100 iterations
## # weights: 75
## initial value 7790.057840
## iter 10 value 4888.285678
## iter 20 value 4754.197881
## iter 30 value 2840.918348
## iter 40 value 2430.885896
## iter 50 value 2215.539502
## iter 60 value 1845.604384
## iter 70 value 1739.287391
## iter 80 value 1671.827057
## iter 90 value 1530.613437
## iter 100 value 1294.620521
## final value 1294.620521
## stopped after 100 iterations
## # weights: 121
## initial value 14976.716729
## iter 10 value 4867.738017
## iter 20 value 4758.994476
## iter 30 value 4754.982095
## iter 40 value 4706.174307
## iter 50 value 3723.866012
## iter 60 value 2884.196015
## iter 70 value 2779.797669
## iter 80 value 2667.952025
## iter 90 value 2508.794016
## iter 100 value 2377.841554
## final value 2377.841554
## stopped after 100 iterations
## # weights: 167
## initial value 7810.424184
## iter 10 value 4754.892924
## iter 20 value 4747.965878
## iter 30 value 4746.590126
## iter 40 value 4644.112532
## iter 50 value 4450.567130
## iter 60 value 3378.146557
## iter 70 value 2928.380963
## iter 80 value 2644.698515
## iter 90 value 2566.152515
## iter 100 value 2209.508344
## final value 2209.508344
## stopped after 100 iterations
## # weights: 52
## initial value 8062.451443
## iter 10 value 4797.355106
## iter 20 value 4762.019674
## iter 30 value 4755.232699
## iter 40 value 4754.119877
## iter 50 value 4236.353990
## iter 60 value 3897.967100
## iter 70 value 3503.904199
## iter 80 value 2746.578236
## iter 90 value 2596.409279
## iter 100 value 2551.583086
## final value 2551.583086
## stopped after 100 iterations
## # weights: 75
## initial value 8385.094880
## iter 10 value 4799.208667
## iter 20 value 4759.742085
## iter 30 value 4758.045552
## iter 40 value 4751.353206
## iter 50 value 4666.127571
## iter 60 value 4626.334474
## iter 70 value 4610.397528
## iter 80 value 4363.846866
## iter 90 value 3736.476561
## iter 100 value 3318.436695
## final value 3318.436695
## stopped after 100 iterations
## # weights: 121
## initial value 9533.636681
## iter 10 value 4775.170381
## iter 20 value 4755.389642
## iter 30 value 4732.756538
## iter 40 value 3278.801743
## iter 50 value 2892.475343
## iter 60 value 2755.141916
## iter 70 value 2551.275877
## iter 80 value 2511.440453
## iter 90 value 2374.116921
## iter 100 value 2122.672184
## final value 2122.672184
## stopped after 100 iterations
## # weights: 167
## initial value 7514.501257
## iter 10 value 4807.748224
## iter 20 value 4754.846322
## iter 30 value 4751.015782
## iter 40 value 4750.424181
## iter 50 value 4734.946179
## iter 60 value 4665.461270
## iter 70 value 4261.804995
## iter 80 value 3456.386062
## iter 90 value 3306.200710
## iter 100 value 2965.206631
## final value 2965.206631
## stopped after 100 iterations
## # weights: 52
## initial value 7221.842650
## iter 10 value 4776.306042
## iter 20 value 4758.265306
## iter 30 value 3915.469686
## iter 40 value 3592.958724
## iter 50 value 3367.529675
## iter 60 value 3166.222497
## iter 70 value 3037.588037
## iter 80 value 2845.678738
## iter 90 value 2440.729839
## iter 100 value 2018.730402
## final value 2018.730402
## stopped after 100 iterations
## # weights: 75
## initial value 9901.662567
## iter 10 value 4855.849589
## iter 20 value 4760.814546
## iter 30 value 4752.485412
## iter 40 value 4747.675210
## iter 50 value 4370.374112
## iter 60 value 3346.061842
## iter 70 value 2995.732135
## iter 80 value 2576.446969
## iter 90 value 2439.061530
## iter 100 value 1774.896658
## final value 1774.896658
## stopped after 100 iterations
## # weights: 121
## initial value 8553.250164
## iter 10 value 4765.256310
## iter 20 value 4758.206326
## iter 30 value 4754.406774
## iter 40 value 4016.637944
## iter 50 value 3477.543030
## iter 60 value 3412.304197
## iter 70 value 3386.124891
## iter 80 value 3265.901626
## iter 90 value 2781.749040
## iter 100 value 2692.603444
## final value 2692.603444
## stopped after 100 iterations
## # weights: 167
## initial value 8121.827649
## iter 10 value 4787.182988
## iter 20 value 4755.508445
## iter 30 value 4750.212651
## iter 40 value 4738.410209
## iter 50 value 3260.321647
## iter 60 value 2874.060760
## iter 70 value 2697.337010
## iter 80 value 2284.823053
## iter 90 value 2115.173037
## iter 100 value 1822.497791
## final value 1822.497791
## stopped after 100 iterations
## # weights: 52
## initial value 7561.854942
## iter 10 value 5052.385612
## iter 20 value 4785.511978
## iter 30 value 4760.779779
## iter 40 value 4759.423268
## final value 4759.051386
## converged
## # weights: 75
## initial value 9687.965478
## iter 10 value 4918.878227
## iter 20 value 4766.599426
## iter 30 value 4742.276899
## iter 40 value 4263.073489
## iter 50 value 2643.611670
## iter 60 value 1969.780214
## iter 70 value 1869.585602
## iter 80 value 1695.773685
## iter 90 value 1609.368787
## iter 100 value 1401.197232
## final value 1401.197232
## stopped after 100 iterations
## # weights: 121
## initial value 8978.942344
## iter 10 value 4931.990328
## iter 20 value 4769.987882
## iter 30 value 4755.870445
## iter 40 value 4754.746034
## iter 50 value 4749.074983
## iter 60 value 4737.948816
## iter 70 value 3808.054550
## iter 80 value 3528.337038
## iter 90 value 3319.130260
## iter 100 value 2596.735043
## final value 2596.735043
## stopped after 100 iterations
## # weights: 167
## initial value 8247.541818
## iter 10 value 4863.375800
## iter 20 value 4755.726684
## iter 30 value 4752.298274
## iter 40 value 4727.773798
## iter 50 value 4697.645889
## iter 60 value 3741.347805
## iter 70 value 2970.835831
## iter 80 value 2704.546605
## iter 90 value 2092.195095
## iter 100 value 2016.460206
## final value 2016.460206
## stopped after 100 iterations
## # weights: 52
## initial value 7495.104766
## iter 10 value 4782.576577
## iter 20 value 4771.515463
## iter 30 value 4448.600541
## iter 40 value 3760.793025
## iter 50 value 3586.453048
## iter 60 value 3362.241995
## iter 70 value 3037.561446
## iter 80 value 2844.297989
## iter 90 value 2825.669180
## iter 100 value 2724.706939
## final value 2724.706939
## stopped after 100 iterations
## # weights: 75
## initial value 7381.337950
## iter 10 value 4799.196605
## iter 20 value 4776.377259
## iter 30 value 3632.643860
## iter 40 value 3306.997832
## iter 50 value 3196.038609
## iter 60 value 2936.991613
## iter 70 value 2791.618834
## iter 80 value 2700.709206
## iter 90 value 2677.358059
## iter 100 value 2354.997895
## final value 2354.997895
## stopped after 100 iterations
## # weights: 121
## initial value 7970.603946
## iter 10 value 4787.922751
## iter 20 value 4766.898857
## iter 30 value 4761.166986
## iter 40 value 4281.758416
## iter 50 value 4132.539268
## iter 60 value 2688.076874
## iter 70 value 2605.539262
## iter 80 value 2596.973407
## iter 90 value 2517.395628
## iter 100 value 2355.784739
## final value 2355.784739
## stopped after 100 iterations
## # weights: 167
## initial value 7217.952244
## iter 10 value 4779.072625
## iter 20 value 4762.661476
## iter 30 value 3654.611214
## iter 40 value 3576.312875
## iter 50 value 3574.976688
## iter 60 value 3342.005262
## iter 70 value 2513.741721
## iter 80 value 2261.714104
## iter 90 value 2101.941684
## iter 100 value 1953.870751
## final value 1953.870751
## stopped after 100 iterations
## # weights: 52
## initial value 9562.333761
## iter 10 value 4866.989164
## iter 20 value 4753.599496
## iter 30 value 4741.859208
## iter 40 value 4740.877328
## final value 4740.877255
## converged
## # weights: 75
## initial value 8097.330657
## iter 10 value 4764.869492
## iter 20 value 4748.112871
## iter 30 value 4090.515999
## iter 40 value 3670.588680
## iter 50 value 3615.547471
## iter 60 value 3495.865524
## iter 70 value 3227.849779
## iter 80 value 3124.721966
## iter 90 value 3070.521738
## iter 100 value 2930.391504
## final value 2930.391504
## stopped after 100 iterations
## # weights: 121
## initial value 7361.751686
## iter 10 value 4815.784382
## iter 20 value 4745.561062
## iter 30 value 4739.911675
## iter 40 value 4738.631901
## iter 50 value 4738.474104
## final value 4738.473439
## converged
## # weights: 167
## initial value 9177.150150
## iter 10 value 4781.411911
## iter 20 value 4780.470257
## iter 30 value 4739.800361
## iter 40 value 4739.141084
## iter 50 value 4738.627612
## iter 60 value 4247.102492
## iter 70 value 3884.304211
## iter 80 value 2949.568374
## iter 90 value 2642.585918
## iter 100 value 2531.050904
## final value 2531.050904
## stopped after 100 iterations
## # weights: 52
## initial value 9198.112021
## iter 10 value 4872.922707
## iter 20 value 4778.881874
## iter 30 value 3288.668951
## iter 40 value 2896.828917
## iter 50 value 2871.021140
## iter 60 value 2811.454706
## iter 70 value 2488.288665
## iter 80 value 1896.115827
## iter 90 value 1510.859026
## iter 100 value 1422.458627
## final value 1422.458627
## stopped after 100 iterations
## # weights: 75
## initial value 9234.457348
## iter 10 value 4795.091552
## iter 20 value 4752.728569
## iter 30 value 4745.002158
## iter 40 value 4744.080749
## iter 50 value 4744.006774
## iter 60 value 4743.987034
## iter 70 value 4615.445184
## iter 80 value 3900.308165
## iter 90 value 3687.393616
## iter 100 value 2166.378536
## final value 2166.378536
## stopped after 100 iterations
## # weights: 121
## initial value 11546.243714
## iter 10 value 4780.671958
## iter 20 value 4740.550753
## iter 30 value 4740.088665
## iter 40 value 4740.079237
## iter 40 value 4740.079196
## iter 40 value 4740.079194
## final value 4740.079194
## converged
## # weights: 167
## initial value 5804.966227
## iter 10 value 4769.184615
## iter 20 value 4742.940078
## iter 30 value 4740.673541
## iter 40 value 4740.247226
## iter 50 value 4712.326666
## iter 60 value 3892.394570
## iter 70 value 3814.580251
## iter 80 value 3795.612707
## iter 90 value 3792.899801
## iter 100 value 3693.886765
## final value 3693.886765
## stopped after 100 iterations
## # weights: 52
## initial value 9399.338615
## iter 10 value 4820.000548
## iter 20 value 4754.109794
## iter 30 value 4747.563464
## iter 40 value 4747.147584
## iter 50 value 4747.061968
## final value 4747.046107
## converged
## # weights: 75
## initial value 9581.454544
## iter 10 value 4774.728684
## iter 20 value 4747.207464
## iter 30 value 4745.350300
## iter 40 value 4273.119561
## iter 50 value 3207.047181
## iter 60 value 2671.009812
## iter 70 value 2578.052538
## iter 80 value 2523.859289
## iter 90 value 2385.981211
## iter 100 value 2342.020481
## final value 2342.020481
## stopped after 100 iterations
## # weights: 121
## initial value 11019.279495
## iter 10 value 4761.206327
## iter 20 value 4744.347210
## iter 30 value 4743.154486
## iter 40 value 4437.219140
## iter 50 value 3402.603278
## iter 60 value 3271.264431
## iter 70 value 3175.538808
## iter 80 value 2837.014086
## iter 90 value 2576.862299
## iter 100 value 1967.367856
## final value 1967.367856
## stopped after 100 iterations
## # weights: 167
## initial value 10810.525312
## iter 10 value 4757.188388
## iter 20 value 4745.228289
## iter 30 value 4743.580333
## iter 40 value 4743.367958
## iter 50 value 4742.871627
## iter 60 value 4736.218311
## iter 70 value 3747.120309
## iter 80 value 3563.547989
## iter 90 value 3080.303883
## iter 100 value 2681.434286
## final value 2681.434286
## stopped after 100 iterations
## # weights: 75
## initial value 11177.025315
## iter 10 value 7280.897393
## iter 20 value 7140.860207
## iter 30 value 7125.684983
## iter 40 value 7124.478281
## iter 50 value 5637.120145
## iter 60 value 4213.676722
## iter 70 value 3813.936334
## iter 80 value 3775.237215
## iter 90 value 3665.148185
## iter 100 value 3618.545923
## final value 3618.545923
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n1_NN1Fit0
## Neural Network
##
## 6835 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4557, 4557, 4556
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.6158158 0.2395594
## 2 0.5 0.7337004 0.4851405
## 2 0.7 0.6979054 0.4164567
## 3 0.3 0.8324916 0.7188841
## 3 0.5 0.8828130 0.8105818
## 3 0.7 0.7787828 0.6299681
## 5 0.3 0.6968811 0.4109647
## 5 0.5 0.6955641 0.4086257
## 5 0.7 0.8311551 0.7099621
## 7 0.3 0.8336570 0.7092632
## 7 0.5 0.7597864 0.6228876
## 7 0.7 0.8122937 0.6815072
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.5.
DryBean_TDA_PC_5.60.5_n1_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.8560772 0.7718302 Fold3
## 2 0.8937665 0.8262725 Fold2
## 3 0.8985953 0.8336428 Fold1
db_tda_pc_5.60.5_n1_nn1_fit_re<-DryBean_TDA_PC_5.60.5_n1_NN1Fit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n1_NN1Fit0)
## a 16-3-6 network with 75 weights
## options were - softmax modelling decay=0.5
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.08 0.00 0.00
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## -0.14 0.01 0.13 -2.13 -2.38 -0.10 -0.05 -0.01 4.00 -0.16
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## -0.14 -0.09 -0.13 0.00 0.00 -0.12 -0.09
## b->o1 h1->o1 h2->o1 h3->o1
## -0.53 -0.53 -0.53 1.04
## b->o2 h1->o2 h2->o2 h3->o2
## -1.26 -1.26 -1.26 1.01
## b->o3 h1->o3 h2->o3 h3->o3
## 1.39 1.39 1.39 -3.56
## b->o4 h1->o4 h2->o4 h3->o4
## -0.48 -0.48 -0.48 -1.00
## b->o5 h1->o5 h2->o5 h3->o5
## -0.10 -0.10 -0.10 5.03
## b->o6 h1->o6 h2->o6 h3->o6
## 0.97 0.97 0.97 -2.51
#vip(DryBean_TDA_PC_5.60.5_n1_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.60.5_n1_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_PC_5.60.5_n1_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n1_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n1_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n1_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 129 8 467 1042 578 20 751
## HOROZ 0 0 0 0 0 0 0
## SEKER 267 148 22 21 0 588 39
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3995
## 95% CI : (0.3844, 0.4147)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2192
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9802
## Specificity 1.00000 1.00000 1.0000 0.3527
## Pos Pred Value NaN NaN NaN 0.3479
## Neg Pred Value 0.90294 0.96176 0.8801 0.9806
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2554
## Detection Prevalence 0.00000 0.00000 0.0000 0.7341
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6665
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9671 0.0000
## Specificity 1.0000 0.8569 1.0000
## Pos Pred Value NaN 0.5419 NaN
## Neg Pred Value 0.8583 0.9933 0.8064
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1441 0.0000
## Detection Prevalence 0.0000 0.2659 0.0000
## Balanced Accuracy 0.5000 0.9120 0.5000
db_tda_pc_5.60.5_n1_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 129 8 467 1042 578 20 751
## HOROZ 0 0 0 0 0 0 0
## SEKER 267 148 22 21 0 588 39
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3995
## 95% CI : (0.3844, 0.4147)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2192
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9802
## Specificity 1.00000 1.00000 1.0000 0.3527
## Pos Pred Value NaN NaN NaN 0.3479
## Neg Pred Value 0.90294 0.96176 0.8801 0.9806
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2554
## Detection Prevalence 0.00000 0.00000 0.0000 0.7341
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6665
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9671 0.0000
## Specificity 1.0000 0.8569 1.0000
## Pos Pred Value NaN 0.5419 NaN
## Neg Pred Value 0.8583 0.9933 0.8064
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1441 0.0000
## Detection Prevalence 0.0000 0.2659 0.0000
## Balanced Accuracy 0.5000 0.9120 0.5000
db_tda_pc_5.60.5_n1_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.995098e-01 2.192476e-01 3.844346e-01 4.147291e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 2.080789e-83 NaN
db_tda_pc_5.60.5_n1_db_nn1_cf0_ov_acc<-db_tda_pc_5.60.5_n1_db_nn1_cf0$overall[1]
db_tda_pc_5.60.5_n1_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9802446 0.3526682 0.3479132 0.9806452 0.3479132
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.9671053 0.8568548 0.5419355 0.9933222 0.5419355
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.0000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.0000000
## Class: CALI 0.0000000 NA 0.11985294 0.0000000
## Class: DERMASON 0.9802446 0.5135535 0.26053922 0.2553922
## Class: HOROZ 0.0000000 NA 0.14166667 0.0000000
## Class: SEKER 0.9671053 0.6946249 0.14901961 0.1441176
## Class: SIRA 0.0000000 NA 0.19362745 0.0000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.0000000 0.5000000
## Class: DERMASON 0.7340686 0.6664564
## Class: HOROZ 0.0000000 0.5000000
## Class: SEKER 0.2659314 0.9119801
## Class: SIRA 0.0000000 0.5000000
db_tda_pc_5.60.5_n1_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n1_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold<-(db_nn1_fit_re - db_tda_pc_5.60.5_n1_nn1_fit_re)
diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold
## Accuracy
## 1 -0.3817304
## 2 -0.1543897
## 3 -0.6382868
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold
## $winLeft
## [1] 0.9916333
##
## $winRope
## [1] 0.008366667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n1_3_fold
## $left
## [1] 0.9290595
##
## $rope
## [1] 0.005602484
##
## $right
## [1] 0.06533799
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold))
#bf_tda_pca_5.60.5_nn1.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n1_3_fold)
## t = -2.8007, df = 2, p-value = 0.1073
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9928675 0.2099296
## sample estimates:
## mean of x
## -0.3914689
### Test set diff
diff_drybean_tda_pca_5.60.5_nn1.n1_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.60.5_n1_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nn1.n1_test
## Accuracy
## -0.02156863
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n1_test
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n1_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n1_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n1_test$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n1_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n1_test
## $winLeft
## [1] 0.8429667
##
## $winRope
## [1] 0.1570333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nn1.n1_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n1_test)) #bf_tda_pca_5.60.5_nn1.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n1_test))
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node2
##DryBean_TDA_PC_5.60.5_n2_NN1Fit0 <- nnet(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n2.vec, size=2, range = 0.6,, type='class')
#Neural Network 1
DryBean_TDA_PC_5.60.5_n2_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n2.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 52
## initial value 9510.224050
## iter 10 value 8551.932276
## iter 20 value 8551.911335
## final value 8551.911123
## converged
## # weights: 75
## initial value 12897.808669
## iter 10 value 8551.711860
## final value 8551.710010
## converged
## # weights: 121
## initial value 10304.114984
## iter 10 value 8588.550128
## iter 20 value 8552.233914
## iter 30 value 8552.215566
## iter 40 value 8551.654436
## final value 8551.642971
## converged
## # weights: 167
## initial value 10401.988943
## iter 10 value 8553.000535
## iter 20 value 8551.199611
## iter 30 value 6902.647419
## iter 40 value 6589.095553
## iter 50 value 6507.675183
## iter 60 value 6232.462542
## iter 70 value 6081.590109
## iter 80 value 5964.429319
## iter 90 value 5626.325948
## iter 100 value 4672.470053
## final value 4672.470053
## stopped after 100 iterations
## # weights: 52
## initial value 10370.035658
## iter 10 value 8564.095952
## iter 20 value 8552.202429
## iter 30 value 8552.159200
## iter 40 value 8552.139652
## iter 40 value 8552.139628
## iter 50 value 8551.955803
## iter 50 value 8551.955795
## iter 50 value 8551.955795
## final value 8551.955795
## converged
## # weights: 75
## initial value 9950.491512
## iter 10 value 8558.954845
## iter 20 value 8552.270665
## iter 30 value 8551.731234
## iter 40 value 8528.520557
## iter 50 value 7393.219541
## iter 60 value 6834.512948
## iter 70 value 6135.811418
## iter 80 value 5589.058472
## iter 90 value 4410.530961
## iter 100 value 3816.362593
## final value 3816.362593
## stopped after 100 iterations
## # weights: 121
## initial value 9630.938323
## iter 10 value 8553.126549
## iter 20 value 8552.168929
## iter 30 value 8552.127758
## iter 40 value 8552.118275
## iter 50 value 8551.740526
## final value 8551.738267
## converged
## # weights: 167
## initial value 9617.307486
## iter 10 value 8642.867463
## iter 20 value 8554.350826
## iter 30 value 8552.200508
## iter 40 value 8552.174648
## iter 50 value 8552.027160
## iter 60 value 8551.964571
## iter 70 value 8551.949616
## iter 80 value 7090.194346
## iter 90 value 6883.054221
## iter 100 value 6692.450898
## final value 6692.450898
## stopped after 100 iterations
## # weights: 52
## initial value 8884.037104
## iter 10 value 8552.531758
## iter 20 value 8552.444678
## iter 30 value 8332.303637
## iter 40 value 7979.866928
## iter 50 value 7519.602547
## iter 60 value 6447.167892
## iter 70 value 6117.156196
## iter 80 value 5342.903916
## iter 90 value 5162.284280
## iter 100 value 4973.997267
## final value 4973.997267
## stopped after 100 iterations
## # weights: 75
## initial value 8615.795225
## iter 10 value 8552.459468
## iter 20 value 8552.444287
## iter 30 value 8552.183663
## iter 40 value 6994.229279
## iter 50 value 6774.014385
## iter 60 value 6535.861109
## iter 70 value 6514.450313
## iter 80 value 6509.221263
## iter 90 value 6507.898857
## iter 100 value 6366.709728
## final value 6366.709728
## stopped after 100 iterations
## # weights: 121
## initial value 11188.652392
## iter 10 value 8552.215043
## iter 20 value 8539.016003
## iter 30 value 7869.109678
## iter 40 value 6720.008132
## iter 50 value 6604.407433
## iter 60 value 6334.588313
## iter 70 value 5721.509527
## iter 80 value 4797.960617
## iter 90 value 4439.839843
## iter 100 value 4325.631844
## final value 4325.631844
## stopped after 100 iterations
## # weights: 167
## initial value 10606.558287
## iter 10 value 8877.174356
## iter 20 value 8546.197937
## iter 30 value 8509.159393
## iter 40 value 8185.512341
## iter 50 value 7392.273813
## iter 60 value 6821.810870
## iter 70 value 6677.645893
## iter 80 value 6294.510796
## iter 90 value 5957.808732
## iter 100 value 5351.184731
## final value 5351.184731
## stopped after 100 iterations
## # weights: 52
## initial value 8920.338314
## iter 10 value 8555.386444
## iter 20 value 8555.130235
## final value 8554.991126
## converged
## # weights: 75
## initial value 9082.366453
## iter 10 value 8554.928035
## final value 8554.923959
## converged
## # weights: 121
## initial value 9888.932703
## iter 10 value 8555.136934
## final value 8555.127987
## converged
## # weights: 167
## initial value 11058.317406
## iter 10 value 8554.931590
## final value 8554.924153
## converged
## # weights: 52
## initial value 10073.202507
## iter 10 value 8579.099813
## iter 20 value 8555.689980
## iter 30 value 8555.396682
## iter 40 value 8404.656378
## iter 50 value 6730.134085
## iter 60 value 6574.084871
## iter 70 value 6402.219090
## iter 80 value 5997.360478
## iter 90 value 5483.764962
## iter 100 value 5243.921408
## final value 5243.921408
## stopped after 100 iterations
## # weights: 75
## initial value 9250.777327
## iter 10 value 8559.217686
## iter 20 value 8555.443773
## iter 30 value 8555.356628
## iter 40 value 6852.894473
## iter 50 value 6806.224001
## iter 60 value 6611.400799
## iter 70 value 6490.197667
## iter 80 value 5790.822679
## iter 90 value 5156.261284
## iter 100 value 4488.026749
## final value 4488.026749
## stopped after 100 iterations
## # weights: 121
## initial value 11251.399511
## iter 10 value 8560.571658
## iter 20 value 8555.238604
## iter 30 value 8555.171982
## iter 30 value 8555.171935
## iter 40 value 8465.547114
## iter 50 value 8157.377044
## iter 60 value 7927.431195
## iter 70 value 7716.068680
## iter 80 value 7365.479377
## iter 90 value 6637.550892
## iter 100 value 6101.693775
## final value 6101.693775
## stopped after 100 iterations
## # weights: 167
## initial value 9613.827103
## iter 10 value 8555.783688
## iter 20 value 8555.177143
## iter 30 value 8555.015052
## iter 40 value 7567.405517
## iter 50 value 7196.515029
## iter 60 value 7039.321772
## iter 70 value 6716.767033
## iter 80 value 6573.395470
## iter 90 value 6404.232587
## iter 100 value 6275.596333
## final value 6275.596333
## stopped after 100 iterations
## # weights: 52
## initial value 12530.764002
## iter 10 value 8555.776871
## final value 8555.661186
## converged
## # weights: 75
## initial value 10654.651317
## iter 10 value 8555.738650
## iter 20 value 8554.564250
## iter 30 value 8453.362052
## iter 40 value 6894.475897
## iter 50 value 6824.710838
## iter 60 value 6286.883912
## iter 70 value 5669.380852
## iter 80 value 5551.380293
## iter 90 value 5537.413279
## iter 100 value 5182.899197
## final value 5182.899197
## stopped after 100 iterations
## # weights: 121
## initial value 12906.822960
## iter 10 value 8556.687980
## final value 8555.192159
## converged
## # weights: 167
## initial value 9380.531494
## iter 10 value 8357.809368
## iter 20 value 7223.392448
## iter 30 value 6470.525232
## iter 40 value 6244.518809
## iter 50 value 6144.357614
## iter 60 value 6052.569350
## iter 70 value 5978.510284
## iter 80 value 5895.591544
## iter 90 value 5854.295798
## iter 100 value 5773.489521
## final value 5773.489521
## stopped after 100 iterations
## # weights: 52
## initial value 9779.016473
## iter 10 value 8555.125414
## final value 8555.125175
## converged
## # weights: 75
## initial value 10356.258885
## iter 10 value 8559.375937
## iter 20 value 8555.182605
## iter 30 value 8555.054494
## iter 40 value 7138.503194
## iter 50 value 7005.614156
## iter 60 value 6415.668823
## iter 70 value 5897.983104
## iter 80 value 5290.288800
## iter 90 value 5067.624385
## iter 100 value 4882.627919
## final value 4882.627919
## stopped after 100 iterations
## # weights: 121
## initial value 10759.765599
## iter 10 value 8554.924743
## final value 8554.924161
## converged
## # weights: 167
## initial value 13314.080400
## iter 10 value 8679.493919
## iter 20 value 8555.179411
## iter 30 value 8555.008589
## iter 40 value 8554.884572
## final value 8554.883598
## converged
## # weights: 52
## initial value 9649.388525
## iter 10 value 8581.293677
## iter 20 value 8556.191017
## iter 30 value 8555.804201
## iter 40 value 8555.732310
## iter 50 value 8144.668793
## iter 60 value 7491.173230
## iter 70 value 7202.784729
## iter 80 value 6070.403230
## iter 90 value 5664.597954
## iter 100 value 5048.108903
## final value 5048.108903
## stopped after 100 iterations
## # weights: 75
## initial value 9324.587160
## iter 10 value 8559.513898
## iter 20 value 8555.106623
## iter 30 value 8555.058700
## final value 8555.058077
## converged
## # weights: 121
## initial value 10556.762092
## iter 10 value 8569.409393
## iter 20 value 8555.035982
## final value 8554.994468
## converged
## # weights: 167
## initial value 12840.405637
## iter 10 value 8571.580868
## iter 20 value 8555.148125
## iter 30 value 8554.992137
## final value 8554.991429
## converged
## # weights: 52
## initial value 9344.674675
## iter 10 value 8562.445928
## iter 20 value 8556.327418
## iter 30 value 7175.774693
## iter 40 value 6952.845635
## iter 50 value 6707.306684
## iter 60 value 6529.058284
## iter 70 value 6303.663139
## iter 80 value 4943.056681
## iter 90 value 3752.148866
## iter 100 value 3481.648229
## final value 3481.648229
## stopped after 100 iterations
## # weights: 75
## initial value 9339.762172
## iter 10 value 8555.367346
## iter 20 value 8555.346406
## iter 20 value 8555.346401
## iter 30 value 7803.740705
## iter 40 value 6891.193620
## iter 50 value 6747.730341
## iter 60 value 6551.091725
## iter 70 value 6417.308365
## iter 80 value 6164.951077
## iter 90 value 6066.749675
## iter 100 value 5817.884667
## final value 5817.884667
## stopped after 100 iterations
## # weights: 121
## initial value 9588.226406
## iter 10 value 8555.661125
## iter 20 value 8555.352123
## final value 8555.348867
## converged
## # weights: 167
## initial value 9413.684780
## iter 10 value 8555.063827
## iter 20 value 7663.898303
## iter 30 value 7234.722826
## iter 40 value 7096.309521
## iter 50 value 6809.715037
## iter 60 value 6692.208853
## iter 70 value 5810.491526
## iter 80 value 5463.737261
## iter 90 value 5334.245217
## iter 100 value 5215.628783
## final value 5215.628783
## stopped after 100 iterations
## # weights: 167
## initial value 14113.844856
## iter 10 value 12832.366992
## iter 20 value 12267.214770
## iter 30 value 11750.226298
## iter 40 value 10770.548785
## iter 50 value 10055.033387
## iter 60 value 9262.360212
## iter 70 value 8044.364602
## iter 80 value 7709.588795
## iter 90 value 7615.438743
## iter 100 value 7356.710004
## final value 7356.710004
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n2_NN1Fit0
## Neural Network
##
## 8024 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5348, 5350, 5350
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.3278914 0.0000000
## 2 0.5 0.5051539 0.3044746
## 2 0.7 0.5630479 0.3790604
## 3 0.3 0.4296116 0.1671979
## 3 0.5 0.5792312 0.4120493
## 3 0.7 0.5657092 0.3899554
## 5 0.3 0.3278914 0.0000000
## 5 0.5 0.3851090 0.1192024
## 5 0.7 0.4431131 0.1954446
## 7 0.3 0.4490922 0.1971020
## 7 0.5 0.4373213 0.2108351
## 7 0.7 0.6379645 0.5075824
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_PC_5.60.5_n2_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.6342558 0.5155438 Fold2
## 2 0.6244395 0.4808225 Fold1
## 3 0.6551982 0.5263808 Fold3
db_tda_pc_5.60.5_n2_nn1_fit_re<-DryBean_TDA_PC_5.60.5_n2_NN1Fit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n2_NN1Fit0)
## a 16-7-6 network with 167 weights
## options were - softmax modelling decay=0.7
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.00 0.10 1.10 0.98 -2.07 0.02 0.01 -0.11 -0.91 0.03
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 -0.01 0.00 0.00 0.00 -0.01 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## -0.05 -1.68 -2.72 -3.88 -2.05 -0.06 -0.09 1.75 -3.42 0.25
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## -0.05 -0.04 -0.03 0.00 0.00 -0.02 -0.05
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4 i9->h4
## 0.00 0.29 0.05 0.02 0.02 0.00 0.00 0.27 0.02 0.00
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5 i9->h5
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6 i9->h6
## -0.09 -0.11 0.08 0.47 1.15 0.07 -0.20 0.15 -8.42 0.47
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6
## -0.09 -0.13 -0.07 0.00 0.00 -0.01 -0.12
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7 i9->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1
## -2.38 -0.92 5.17 0.02 -0.02 -2.39 4.11 0.01
## b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2
## -0.52 -1.74 3.54 0.12 -0.04 -0.51 1.29 -0.03
## b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3
## 1.79 1.52 -5.98 -0.22 0.16 1.81 -5.49 -0.01
## b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4
## -0.56 1.80 0.30 0.48 0.00 -0.56 0.18 0.03
## b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5
## 0.55 -1.54 -3.31 -0.24 -0.16 0.56 1.15 -0.21
## b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6
## 1.11 0.90 0.28 -0.16 0.06 1.09 -1.25 0.22
#vip(DryBean_TDA_PC_5.60.5_n2_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.60.5_n2_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_PC_5.60.5_n2_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n2_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n2_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n2_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 382 156 469 2 178 9 29
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 1 0 0 0 0
## DERMASON 0 0 0 1012 18 343 101
## HOROZ 0 0 0 0 0 0 0
## SEKER 6 0 3 5 0 228 52
## SIRA 8 0 16 44 382 28 608
##
## Overall Statistics
##
## Accuracy : 0.5468
## 95% CI : (0.5314, 0.5622)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4436
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.96465 0.00000 0.0020450 0.9520
## Specificity 0.77117 1.00000 1.0000000 0.8469
## Pos Pred Value 0.31184 NaN 1.0000000 0.6866
## Neg Pred Value 0.99510 0.96176 0.8803628 0.9804
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.09363 0.00000 0.0002451 0.2480
## Detection Prevalence 0.30025 0.00000 0.0002451 0.3613
## Balanced Accuracy 0.86791 0.50000 0.5010225 0.8994
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.37500 0.7696
## Specificity 1.0000 0.98099 0.8547
## Pos Pred Value NaN 0.77551 0.5599
## Neg Pred Value 0.8583 0.89963 0.9392
## Prevalence 0.1417 0.14902 0.1936
## Detection Rate 0.0000 0.05588 0.1490
## Detection Prevalence 0.0000 0.07206 0.2662
## Balanced Accuracy 0.5000 0.67800 0.8122
db_tda_pc_5.60.5_n2_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 382 156 469 2 178 9 29
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 1 0 0 0 0
## DERMASON 0 0 0 1012 18 343 101
## HOROZ 0 0 0 0 0 0 0
## SEKER 6 0 3 5 0 228 52
## SIRA 8 0 16 44 382 28 608
##
## Overall Statistics
##
## Accuracy : 0.5468
## 95% CI : (0.5314, 0.5622)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4436
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.96465 0.00000 0.0020450 0.9520
## Specificity 0.77117 1.00000 1.0000000 0.8469
## Pos Pred Value 0.31184 NaN 1.0000000 0.6866
## Neg Pred Value 0.99510 0.96176 0.8803628 0.9804
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.09363 0.00000 0.0002451 0.2480
## Detection Prevalence 0.30025 0.00000 0.0002451 0.3613
## Balanced Accuracy 0.86791 0.50000 0.5010225 0.8994
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.37500 0.7696
## Specificity 1.0000 0.98099 0.8547
## Pos Pred Value NaN 0.77551 0.5599
## Neg Pred Value 0.8583 0.89963 0.9392
## Prevalence 0.1417 0.14902 0.1936
## Detection Rate 0.0000 0.05588 0.1490
## Detection Prevalence 0.0000 0.07206 0.2662
## Balanced Accuracy 0.5000 0.67800 0.8122
db_tda_pc_5.60.5_n2_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5468137 0.4435512 0.5313874 0.5621729 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n2_db_nn1_cf0_ov_acc<-db_tda_pc_5.60.5_n2_db_nn1_cf0$overall[1]
db_tda_pc_5.60.5_n2_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.96464646 0.7711726 0.3118367 0.9950963 0.3118367
## Class: BOMBAY 0.00000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.00204499 1.0000000 1.0000000 0.8803628 1.0000000
## Class: DERMASON 0.95202258 0.8468677 0.6865672 0.9804298 0.6865672
## Class: HOROZ 0.00000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.37500000 0.9809908 0.7755102 0.8996302 0.7755102
## Class: SIRA 0.76962025 0.8547112 0.5598527 0.9392118 0.5598527
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.96464646 0.471314004 0.09705882 0.093627451
## Class: BOMBAY 0.00000000 NA 0.03823529 0.000000000
## Class: CALI 0.00204499 0.004081633 0.11985294 0.000245098
## Class: DERMASON 0.95202258 0.797792669 0.26053922 0.248039216
## Class: HOROZ 0.00000000 NA 0.14166667 0.000000000
## Class: SEKER 0.37500000 0.505543237 0.14901961 0.055882353
## Class: SIRA 0.76962025 0.648187633 0.19362745 0.149019608
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.300245098 0.8679096
## Class: BOMBAY 0.000000000 0.5000000
## Class: CALI 0.000245098 0.5010225
## Class: DERMASON 0.361274510 0.8994452
## Class: HOROZ 0.000000000 0.5000000
## Class: SEKER 0.072058824 0.6779954
## Class: SIRA 0.266176471 0.8121657
db_tda_pc_5.60.5_n2_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n2_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold<-(db_nn1_fit_re - db_tda_pc_5.60.5_n2_nn1_fit_re)
diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold
## Accuracy
## 1 -0.1599089
## 2 0.1149373
## 3 -0.3948897
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold
## $winLeft
## [1] 0.8771667
##
## $winRope
## [1] 0.0152
##
## $winRight
## [1] 0.1076333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n2_3_fold
## $left
## [1] 0.7469057
##
## $rope
## [1] 0.02588687
##
## $right
## [1] 0.2272075
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold))
#bf_tda_pca_5.60.5_nn1.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n2_3_fold)
## t = -0.99522, df = 2, p-value = 0.4245
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7805057 0.4872648
## sample estimates:
## mean of x
## -0.1466205
### Test set diff
diff_drybean_tda_pca_5.60.5_nn1.n2_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.60.5_n2_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nn1.n2_test
## Accuracy
## -0.1688725
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n2_test
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n2_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n2_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n2_test$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n2_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n2_test
## $winLeft
## [1] 0.8369667
##
## $winRope
## [1] 0.1630333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nn1.n2_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n2_test)) #bf_tda_pca_5.60.5_nn1.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n2_test))
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node3
#Neural Network 1
DryBean_TDA_PC_5.60.5_n3_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n3.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 52
## initial value 7862.207380
## iter 10 value 4354.956656
## iter 20 value 4215.743261
## iter 30 value 4214.812584
## iter 40 value 3664.957510
## iter 50 value 3267.787622
## iter 60 value 2518.195530
## iter 70 value 2371.900467
## iter 80 value 2127.590550
## iter 90 value 1612.027331
## iter 100 value 1411.419480
## final value 1411.419480
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 75
## initial value 8116.512427
## iter 10 value 4245.566197
## iter 20 value 4245.296254
## iter 30 value 4243.038650
## iter 40 value 3644.261259
## iter 50 value 3202.003922
## iter 60 value 2833.315184
## iter 70 value 2498.643586
## iter 80 value 2415.644926
## iter 90 value 2380.590779
## iter 100 value 2326.963630
## final value 2326.963630
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 121
## initial value 7508.979846
## iter 10 value 4290.124208
## iter 20 value 4238.824824
## iter 30 value 4234.138082
## iter 40 value 4230.641752
## iter 50 value 4226.012185
## iter 60 value 4205.322502
## iter 70 value 2598.561240
## iter 80 value 2455.536675
## iter 90 value 2369.656256
## iter 100 value 2340.592071
## final value 2340.592071
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 167
## initial value 6360.126308
## iter 10 value 4241.667201
## iter 20 value 4237.439497
## iter 30 value 4232.333920
## iter 40 value 3852.765716
## iter 50 value 3775.341519
## iter 60 value 3713.470766
## iter 70 value 3093.197681
## iter 80 value 2879.369625
## iter 90 value 2587.840848
## iter 100 value 2491.115278
## final value 2491.115278
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 52
## initial value 5874.053784
## iter 10 value 4248.787788
## iter 20 value 4241.000708
## iter 30 value 4234.272036
## iter 40 value 4234.134010
## iter 50 value 4233.973171
## final value 4233.969767
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 75
## initial value 6655.826698
## iter 10 value 4355.234017
## iter 20 value 4217.507813
## iter 30 value 3365.752181
## iter 40 value 3257.456215
## iter 50 value 2983.990042
## iter 60 value 2589.512176
## iter 70 value 2404.462386
## iter 80 value 2240.721656
## iter 90 value 1994.557083
## iter 100 value 1742.592612
## final value 1742.592612
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 121
## initial value 7257.887238
## iter 10 value 4267.923604
## iter 20 value 4258.319925
## iter 30 value 3979.475692
## iter 40 value 3533.846341
## iter 50 value 3382.434944
## iter 60 value 2926.929542
## iter 70 value 2734.353867
## iter 80 value 2454.828441
## iter 90 value 2350.436319
## iter 100 value 2154.843862
## final value 2154.843862
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 167
## initial value 6403.811085
## iter 10 value 4279.343103
## iter 20 value 4236.243765
## iter 30 value 4234.831105
## iter 40 value 4228.381119
## iter 50 value 4164.398012
## iter 60 value 3329.027094
## iter 70 value 3220.261381
## iter 80 value 2840.034512
## iter 90 value 2491.874738
## iter 100 value 2399.436862
## final value 2399.436862
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 52
## initial value 5770.444073
## iter 10 value 4241.502198
## iter 20 value 4241.495733
## iter 30 value 4237.533360
## iter 40 value 4096.062190
## iter 50 value 4021.839159
## iter 60 value 2909.070220
## iter 70 value 2411.836387
## iter 80 value 1598.887350
## iter 90 value 1459.086375
## iter 100 value 1339.059093
## final value 1339.059093
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 75
## initial value 5376.852633
## iter 10 value 4244.057942
## iter 20 value 4241.638811
## iter 30 value 4238.548647
## iter 40 value 4238.309407
## iter 40 value 4238.309407
## iter 40 value 4238.309389
## final value 4238.309389
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 121
## initial value 6234.514364
## iter 10 value 4257.213815
## iter 20 value 4245.829023
## iter 30 value 4236.625551
## iter 40 value 4236.619418
## iter 40 value 4236.619415
## iter 50 value 4236.193655
## iter 60 value 4235.105533
## iter 70 value 4163.001752
## iter 80 value 3901.228138
## iter 90 value 3843.850900
## iter 100 value 3769.110087
## final value 3769.110087
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'DERMASON' is empty
## # weights: 167
## initial value 8691.704546
## iter 10 value 4257.580870
## iter 20 value 4240.902534
## iter 30 value 2919.317152
## iter 40 value 2607.304046
## iter 50 value 2371.593587
## iter 60 value 2213.420305
## iter 70 value 2147.862180
## iter 80 value 1996.041360
## iter 90 value 1914.020822
## iter 100 value 1853.127407
## final value 1853.127407
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 52
## initial value 5365.362172
## iter 10 value 4235.291801
## iter 20 value 4233.002688
## iter 30 value 4232.959035
## iter 40 value 4030.302513
## iter 50 value 3935.838807
## iter 60 value 3313.096848
## iter 70 value 2413.462614
## iter 80 value 2228.429500
## iter 90 value 2082.397758
## iter 100 value 1953.276556
## final value 1953.276556
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 75
## initial value 6087.713873
## iter 10 value 4255.720703
## iter 20 value 4234.584964
## iter 30 value 4233.236197
## iter 40 value 3937.347068
## iter 50 value 3770.083556
## iter 60 value 3192.790521
## iter 70 value 2487.796522
## iter 80 value 2284.949769
## iter 90 value 2220.142429
## iter 100 value 1743.269444
## final value 1743.269444
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 121
## initial value 10252.439071
## iter 10 value 4245.156231
## iter 20 value 4202.740282
## iter 30 value 4051.064773
## iter 40 value 4025.743775
## iter 50 value 3888.665839
## iter 60 value 3168.022327
## iter 70 value 3012.155667
## iter 80 value 2690.560008
## iter 90 value 2219.122920
## iter 100 value 1974.596750
## final value 1974.596750
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 167
## initial value 5875.857607
## iter 10 value 4260.066339
## iter 20 value 4252.103008
## iter 30 value 4217.154079
## iter 40 value 3189.719439
## iter 50 value 2709.387739
## iter 60 value 2162.203198
## iter 70 value 1747.939617
## iter 80 value 1612.939610
## iter 90 value 1561.819994
## iter 100 value 1439.624976
## final value 1439.624976
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 52
## initial value 7776.421021
## iter 10 value 4259.702625
## iter 20 value 4246.889274
## iter 30 value 4240.452428
## iter 40 value 3414.009058
## iter 50 value 2823.032672
## iter 60 value 2281.202485
## iter 70 value 2218.968825
## iter 80 value 2146.775026
## iter 90 value 1928.747479
## iter 100 value 1832.842219
## final value 1832.842219
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 75
## initial value 8664.862161
## iter 10 value 4277.742685
## iter 20 value 4233.531679
## iter 30 value 4233.219379
## iter 40 value 4232.558381
## final value 4232.555570
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 121
## initial value 7974.384335
## iter 10 value 4373.021065
## iter 20 value 4259.881622
## iter 30 value 4258.367128
## iter 40 value 4252.564636
## iter 50 value 4230.460069
## iter 60 value 4081.325667
## iter 70 value 3772.276657
## iter 80 value 3220.503714
## iter 90 value 2571.862203
## iter 100 value 2528.183612
## final value 2528.183612
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 167
## initial value 10745.657631
## iter 10 value 4605.114115
## iter 20 value 4308.131178
## iter 30 value 4302.775233
## iter 40 value 4292.148416
## iter 50 value 4120.051660
## iter 60 value 3667.886678
## iter 70 value 2937.142869
## iter 80 value 2497.619995
## iter 90 value 2275.947602
## iter 100 value 2195.248395
## final value 2195.248395
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 52
## initial value 6339.657547
## iter 10 value 4241.808984
## iter 20 value 4240.225154
## iter 30 value 4235.049021
## iter 40 value 3139.675610
## iter 50 value 2831.182944
## iter 60 value 2476.736943
## iter 70 value 2298.750487
## iter 80 value 2237.382126
## iter 90 value 2184.442918
## iter 100 value 2082.868478
## final value 2082.868478
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 75
## initial value 6730.541122
## iter 10 value 4759.494573
## iter 20 value 4246.888969
## iter 30 value 4238.386917
## iter 40 value 4222.769439
## iter 50 value 3884.969620
## iter 60 value 2707.629103
## iter 70 value 2064.399476
## iter 80 value 1716.173802
## iter 90 value 1540.937233
## iter 100 value 1482.621605
## final value 1482.621605
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 121
## initial value 5730.031646
## iter 10 value 4246.441972
## iter 20 value 4234.780964
## iter 30 value 4233.049157
## iter 40 value 4232.696660
## iter 50 value 3671.811764
## iter 60 value 3557.890524
## iter 70 value 3509.855339
## iter 80 value 3132.826302
## iter 90 value 2725.371143
## iter 100 value 2571.491594
## final value 2571.491594
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'SEKER' is empty
## # weights: 167
## initial value 5945.945848
## iter 10 value 4240.715069
## iter 20 value 4140.071328
## iter 30 value 3904.050380
## iter 40 value 3780.680634
## iter 50 value 3605.530761
## iter 60 value 3008.089666
## iter 70 value 2560.956424
## iter 80 value 2359.942896
## iter 90 value 2086.300816
## iter 100 value 1948.836200
## final value 1948.836200
## stopped after 100 iterations
## # weights: 55
## initial value 6601.807023
## iter 10 value 4245.703555
## iter 20 value 4245.210453
## iter 30 value 4216.462083
## iter 40 value 3665.218847
## iter 50 value 3496.790617
## iter 60 value 3481.590046
## iter 70 value 2979.136175
## iter 80 value 2630.984492
## iter 90 value 2501.548157
## iter 100 value 2372.482860
## final value 2372.482860
## stopped after 100 iterations
## # weights: 79
## initial value 6932.287807
## iter 10 value 4347.163498
## iter 20 value 4272.970535
## iter 30 value 4269.441714
## iter 40 value 3786.041215
## iter 50 value 3477.993794
## iter 60 value 2999.262775
## iter 70 value 2850.153239
## iter 80 value 2613.065258
## iter 90 value 2330.078006
## iter 100 value 2048.429627
## final value 2048.429627
## stopped after 100 iterations
## # weights: 127
## initial value 6626.477301
## iter 10 value 4285.174454
## iter 20 value 4277.934733
## iter 30 value 4274.717687
## iter 40 value 3233.826222
## iter 50 value 2614.562464
## iter 60 value 2429.843314
## iter 70 value 2388.199433
## iter 80 value 2349.003525
## iter 90 value 2325.129578
## iter 100 value 2319.658810
## final value 2319.658810
## stopped after 100 iterations
## # weights: 175
## initial value 4772.049384
## iter 10 value 4237.678659
## iter 20 value 4236.455229
## iter 30 value 4236.152807
## iter 40 value 4235.934361
## iter 50 value 4081.077298
## iter 60 value 3981.335514
## iter 70 value 3372.587851
## iter 80 value 2629.155033
## iter 90 value 2440.592264
## iter 100 value 2410.866862
## final value 2410.866862
## stopped after 100 iterations
## # weights: 55
## initial value 6467.617490
## iter 10 value 4273.710107
## iter 20 value 4251.526745
## iter 30 value 4222.283616
## iter 40 value 3588.913087
## iter 50 value 3449.088249
## iter 60 value 3265.591821
## iter 70 value 3010.091173
## iter 80 value 2552.672586
## iter 90 value 2302.815339
## iter 100 value 1740.730393
## final value 1740.730393
## stopped after 100 iterations
## # weights: 79
## initial value 7667.102476
## iter 10 value 4436.351588
## iter 20 value 4260.992080
## iter 30 value 4242.911974
## iter 40 value 4204.216577
## iter 50 value 3690.595254
## iter 60 value 3590.618816
## iter 70 value 3392.915376
## iter 80 value 3292.603295
## iter 90 value 3245.377923
## iter 100 value 2972.713590
## final value 2972.713590
## stopped after 100 iterations
## # weights: 127
## initial value 6695.725108
## iter 10 value 4261.146017
## iter 20 value 4251.807871
## iter 30 value 4251.587108
## iter 40 value 4246.018949
## iter 50 value 4244.597106
## iter 60 value 3849.503058
## iter 70 value 2883.810039
## iter 80 value 2824.505696
## iter 90 value 2741.100369
## iter 100 value 2627.648528
## final value 2627.648528
## stopped after 100 iterations
## # weights: 175
## initial value 8216.017323
## iter 10 value 4444.003521
## iter 20 value 4294.218242
## iter 30 value 4288.516238
## iter 40 value 3768.759299
## iter 50 value 2774.172367
## iter 60 value 2606.240655
## iter 70 value 2408.590742
## iter 80 value 2085.297155
## iter 90 value 1671.081176
## iter 100 value 1614.257104
## final value 1614.257104
## stopped after 100 iterations
## # weights: 55
## initial value 6303.534786
## iter 10 value 4251.921408
## iter 20 value 4249.208917
## iter 30 value 3887.508054
## iter 40 value 3678.199165
## iter 50 value 3017.383212
## iter 60 value 2887.428249
## iter 70 value 2662.604462
## iter 80 value 2566.420485
## iter 90 value 2556.893148
## iter 100 value 2478.994419
## final value 2478.994419
## stopped after 100 iterations
## # weights: 79
## initial value 6717.876767
## iter 10 value 4314.404734
## iter 20 value 4255.705943
## iter 30 value 4252.228363
## iter 40 value 4250.180836
## iter 50 value 4247.052634
## iter 60 value 4246.341941
## iter 70 value 4245.871966
## iter 80 value 4233.631865
## iter 90 value 3994.559393
## iter 100 value 3344.485623
## final value 3344.485623
## stopped after 100 iterations
## # weights: 127
## initial value 5664.072115
## iter 10 value 4263.709528
## iter 20 value 4254.331149
## iter 30 value 4254.136116
## iter 40 value 4216.563972
## iter 50 value 3520.814265
## iter 60 value 3232.311023
## iter 70 value 2858.647759
## iter 80 value 2618.187752
## iter 90 value 2087.649508
## iter 100 value 1576.320169
## final value 1576.320169
## stopped after 100 iterations
## # weights: 175
## initial value 8407.283195
## iter 10 value 4276.541490
## iter 20 value 4273.555792
## iter 30 value 4229.481289
## iter 40 value 4133.833692
## iter 50 value 3389.045282
## iter 60 value 3065.491835
## iter 70 value 2907.047723
## iter 80 value 2795.949305
## iter 90 value 2623.341038
## iter 100 value 2338.119377
## final value 2338.119377
## stopped after 100 iterations
## # weights: 55
## initial value 8823.655070
## iter 10 value 6378.051995
## iter 20 value 6371.367664
## iter 30 value 5184.679088
## iter 40 value 3876.332824
## iter 50 value 3587.226504
## iter 60 value 3556.793864
## iter 70 value 3462.931154
## iter 80 value 3357.108377
## iter 90 value 2767.396913
## iter 100 value 2085.451202
## final value 2085.451202
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n3_NN1Fit0
## Neural Network
##
## 5008 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 3339, 3338, 3339
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.6768840 0.5629723
## 2 0.5 0.5724238 0.5303819
## 2 0.7 0.6826762 0.5694244
## 3 0.3 0.6738859 0.5742623
## 3 0.5 0.5102103 0.2904621
## 3 0.7 0.5083118 0.4229458
## 5 0.3 0.6117775 0.4766277
## 5 0.5 0.6076133 0.4575038
## 5 0.7 0.5670705 0.4726360
## 7 0.3 0.6369307 0.5235983
## 7 0.5 0.6613152 0.5554302
## 7 0.7 0.6529325 0.5399231
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 2 and decay = 0.7.
DryBean_TDA_PC_5.60.5_n3_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.6878370 0.5225931 Fold3
## 2 0.8401198 0.7619397 Fold2
## 3 0.5200719 0.4237405 Fold1
db_tda_pc_5.60.5_n3_nn1_fit_re<-DryBean_TDA_PC_5.60.5_n3_NN1Fit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n3_NN1Fit0)
## a 16-2-7 network with 55 weights
## options were - softmax modelling decay=0.7
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.35 0.00 -0.01 -0.24 -0.22 0.32 0.38 0.00 0.42 1.90
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.34 0.21 0.23 0.01 0.00 0.12 0.30
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## -0.16 0.02 0.54 -1.09 -0.95 0.81 -0.63 -0.02 0.31 -0.28
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## -0.16 -0.22 0.03 0.00 0.00 0.29 0.08
## b->o1 h1->o1 h2->o1
## -1.79 3.81 3.73
## b->o2 h1->o2 h2->o2
## -2.60 4.69 -0.84
## b->o3 h1->o3 h2->o3
## 0.89 3.54 -1.57
## b->o4 h1->o4 h2->o4
## -1.64 -0.97 -0.55
## b->o5 h1->o5 h2->o5
## 4.97 -8.11 -0.51
## b->o6 h1->o6 h2->o6
## -1.89 -1.06 0.00
## b->o7 h1->o7 h2->o7
## 2.07 -1.89 -0.26
#vip(DryBean_TDA_PC_5.60.5_n3_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.60.5_n3_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_PC_5.60.5_n3_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n3_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n3_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.60.5_n3_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 327 7 18 5 4 78 20
## BOMBAY 0 0 0 0 0 0 0
## CALI 62 149 459 96 9 527 396
## DERMASON 0 0 0 0 0 0 0
## HOROZ 7 0 12 962 565 3 374
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3311
## 95% CI : (0.3167, 0.3458)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2333
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.82576 0.00000 0.9387 0.0000
## Specificity 0.96417 1.00000 0.6550 1.0000
## Pos Pred Value 0.71242 NaN 0.2703 NaN
## Neg Pred Value 0.98094 0.96176 0.9874 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08015 0.00000 0.1125 0.0000
## Detection Prevalence 0.11250 0.00000 0.4162 0.0000
## Balanced Accuracy 0.89496 0.50000 0.7968 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9775 0.000 0.0000
## Specificity 0.6122 1.000 1.0000
## Pos Pred Value 0.2938 NaN NaN
## Neg Pred Value 0.9940 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1385 0.000 0.0000
## Detection Prevalence 0.4713 0.000 0.0000
## Balanced Accuracy 0.7949 0.500 0.5000
db_tda_pc_5.60.5_n3_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 327 7 18 5 4 78 20
## BOMBAY 0 0 0 0 0 0 0
## CALI 62 149 459 96 9 527 396
## DERMASON 0 0 0 0 0 0 0
## HOROZ 7 0 12 962 565 3 374
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3311
## 95% CI : (0.3167, 0.3458)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2333
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.82576 0.00000 0.9387 0.0000
## Specificity 0.96417 1.00000 0.6550 1.0000
## Pos Pred Value 0.71242 NaN 0.2703 NaN
## Neg Pred Value 0.98094 0.96176 0.9874 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08015 0.00000 0.1125 0.0000
## Detection Prevalence 0.11250 0.00000 0.4162 0.0000
## Balanced Accuracy 0.89496 0.50000 0.7968 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9775 0.000 0.0000
## Specificity 0.6122 1.000 1.0000
## Pos Pred Value 0.2938 NaN NaN
## Neg Pred Value 0.9940 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1385 0.000 0.0000
## Detection Prevalence 0.4713 0.000 0.0000
## Balanced Accuracy 0.7949 0.500 0.5000
db_tda_pc_5.60.5_n3_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.311275e-01 2.333225e-01 3.166894e-01 3.458077e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 7.904289e-24 NaN
db_tda_pc_5.60.5_n3_db_nn1_cf0_ov_acc<-db_tda_pc_5.60.5_n3_db_nn1_cf0$overall[1]
db_tda_pc_5.60.5_n3_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8257576 0.9641694 0.7124183 0.9809445 0.7124183
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9386503 0.6549708 0.2703180 0.9874055 0.2703180
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.9775087 0.6122216 0.2938118 0.9939731 0.2938118
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8257576 0.7649123 0.09705882 0.08014706
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.9386503 0.4197531 0.11985294 0.11250000
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.9775087 0.4518193 0.14166667 0.13848039
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.1125000 0.8949635
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.4161765 0.7968105
## Class: DERMASON 0.0000000 0.5000000
## Class: HOROZ 0.4713235 0.7948651
## Class: SEKER 0.0000000 0.5000000
## Class: SIRA 0.0000000 0.5000000
db_tda_pc_5.60.5_n3_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n3_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold<-(db_nn1_fit_re - db_tda_pc_5.60.5_n3_nn1_fit_re)
diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold
## Accuracy
## 1 -0.2134902
## 2 -0.1007430
## 3 -0.2597634
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold
## $winLeft
## [1] 0.9912667
##
## $winRope
## [1] 0.008733333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n3_3_fold
## $left
## [1] 0.9601206
##
## $rope
## [1] 0.006800542
##
## $right
## [1] 0.03307883
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold))
#bf_tda_pca_5.60.5_nn1.n3_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n3_3_fold)
## t = -4.0517, df = 2, p-value = 0.05586
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.39451745 0.01185306
## sample estimates:
## mean of x
## -0.1913322
### Test set diff
diff_drybean_tda_pca_5.60.5_nn1.n3_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.60.5_n3_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nn1.n3_test
## Accuracy
## 0.04681373
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n3_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n3_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n3_test$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n3_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1619667
##
## $winRight
## [1] 0.8380333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nn1.n3_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n3_test)) #bf_tda_pca_5.60.5_nn1.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n3_test))
##Node4
#Neural Network 1
DryBean_TDA_PC_5.60.5_n4_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n4.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 46
## initial value 781.157386
## iter 10 value 654.412722
## iter 20 value 591.952853
## iter 30 value 375.320398
## iter 40 value 325.363131
## iter 50 value 298.583475
## iter 60 value 284.124123
## iter 70 value 274.209218
## iter 80 value 264.162752
## iter 90 value 263.003243
## iter 100 value 256.967497
## final value 256.967497
## stopped after 100 iterations
## # weights: 67
## initial value 907.168450
## iter 10 value 648.555299
## iter 20 value 574.230526
## iter 30 value 301.103669
## iter 40 value 268.055062
## iter 50 value 266.760695
## iter 60 value 265.822232
## iter 70 value 265.599352
## iter 80 value 265.546500
## iter 90 value 265.487927
## iter 100 value 265.196699
## final value 265.196699
## stopped after 100 iterations
## # weights: 109
## initial value 1365.078957
## iter 10 value 653.988816
## iter 20 value 653.440361
## iter 30 value 638.935823
## iter 40 value 474.244932
## iter 50 value 318.516541
## iter 60 value 306.349529
## iter 70 value 304.301438
## iter 80 value 245.673136
## iter 90 value 223.920005
## iter 100 value 167.569448
## final value 167.569448
## stopped after 100 iterations
## # weights: 151
## initial value 1050.141679
## iter 10 value 646.104367
## iter 20 value 637.644690
## iter 30 value 459.431642
## iter 40 value 284.664088
## iter 50 value 245.420537
## iter 60 value 208.410366
## iter 70 value 207.249712
## iter 80 value 135.164900
## iter 90 value 97.563198
## iter 100 value 78.631712
## final value 78.631712
## stopped after 100 iterations
## # weights: 46
## initial value 1101.276292
## iter 10 value 688.641476
## iter 20 value 675.225731
## iter 30 value 654.197217
## iter 40 value 654.074042
## iter 40 value 654.074042
## iter 50 value 649.967167
## iter 60 value 612.921092
## iter 70 value 293.674058
## iter 80 value 283.686735
## iter 90 value 277.829844
## iter 100 value 275.980094
## final value 275.980094
## stopped after 100 iterations
## # weights: 67
## initial value 931.985092
## iter 10 value 656.073239
## iter 20 value 615.457111
## iter 30 value 546.329696
## iter 40 value 313.466434
## iter 50 value 240.930041
## iter 60 value 216.369174
## iter 70 value 183.530840
## iter 80 value 172.173994
## iter 90 value 157.730702
## iter 100 value 147.977372
## final value 147.977372
## stopped after 100 iterations
## # weights: 109
## initial value 1104.709671
## iter 10 value 654.234156
## iter 20 value 653.533245
## iter 30 value 653.370652
## iter 40 value 290.148660
## iter 50 value 278.067637
## iter 60 value 277.022654
## iter 70 value 275.650572
## iter 80 value 274.520967
## iter 90 value 270.134976
## iter 100 value 231.120309
## final value 231.120309
## stopped after 100 iterations
## # weights: 151
## initial value 953.734340
## iter 10 value 656.430303
## iter 20 value 653.569153
## iter 30 value 653.525547
## iter 40 value 653.519440
## iter 50 value 597.958968
## iter 60 value 415.497595
## iter 70 value 394.188525
## iter 80 value 257.211797
## iter 90 value 228.344332
## iter 100 value 197.928919
## final value 197.928919
## stopped after 100 iterations
## # weights: 46
## initial value 1143.775866
## iter 10 value 658.594307
## iter 20 value 657.652773
## iter 30 value 579.722882
## iter 40 value 413.633549
## iter 50 value 345.128486
## iter 60 value 339.192012
## iter 70 value 301.684734
## iter 80 value 288.634144
## iter 90 value 284.539320
## iter 100 value 284.328989
## final value 284.328989
## stopped after 100 iterations
## # weights: 67
## initial value 1307.641588
## iter 10 value 648.248896
## iter 20 value 527.353269
## iter 30 value 337.408655
## iter 40 value 316.085450
## iter 50 value 292.952961
## iter 60 value 271.602592
## iter 70 value 267.935440
## iter 80 value 267.016746
## iter 90 value 263.231093
## iter 100 value 241.684187
## final value 241.684187
## stopped after 100 iterations
## # weights: 109
## initial value 1397.838360
## iter 10 value 653.671213
## iter 20 value 632.878395
## iter 30 value 629.545679
## iter 40 value 610.451349
## iter 50 value 600.127652
## iter 60 value 314.748352
## iter 70 value 278.615800
## iter 80 value 244.671665
## iter 90 value 164.762337
## iter 100 value 150.156142
## final value 150.156142
## stopped after 100 iterations
## # weights: 151
## initial value 818.685765
## iter 10 value 653.531510
## iter 20 value 644.098154
## iter 30 value 348.754380
## iter 40 value 318.586546
## iter 50 value 288.722486
## iter 60 value 287.545831
## iter 70 value 285.624005
## iter 80 value 282.497880
## iter 90 value 261.241913
## iter 100 value 165.308325
## final value 165.308325
## stopped after 100 iterations
## # weights: 46
## initial value 795.083471
## iter 10 value 659.544522
## iter 20 value 646.861976
## iter 30 value 313.458329
## iter 40 value 281.636427
## iter 50 value 260.226091
## iter 60 value 254.289676
## iter 70 value 191.992197
## iter 80 value 148.240110
## iter 90 value 139.572588
## iter 100 value 134.501064
## final value 134.501064
## stopped after 100 iterations
## # weights: 67
## initial value 751.541902
## iter 10 value 658.615326
## iter 20 value 574.022033
## iter 30 value 494.187262
## iter 40 value 319.160804
## iter 50 value 300.723043
## iter 60 value 277.031022
## iter 70 value 268.462521
## iter 80 value 268.122035
## iter 90 value 264.523726
## iter 100 value 239.356538
## final value 239.356538
## stopped after 100 iterations
## # weights: 109
## initial value 1215.672787
## iter 10 value 660.197715
## iter 20 value 658.483317
## iter 30 value 644.142280
## iter 40 value 512.504107
## iter 50 value 368.632544
## iter 60 value 282.240828
## iter 70 value 273.263260
## iter 80 value 268.740127
## iter 90 value 268.575761
## iter 100 value 267.906098
## final value 267.906098
## stopped after 100 iterations
## # weights: 151
## initial value 712.897442
## iter 10 value 648.468413
## iter 20 value 629.054041
## iter 30 value 535.903067
## iter 40 value 270.866093
## iter 50 value 260.358704
## iter 60 value 249.826802
## iter 70 value 205.579921
## iter 80 value 157.151883
## iter 90 value 144.672671
## iter 100 value 113.056865
## final value 113.056865
## stopped after 100 iterations
## # weights: 46
## initial value 866.586296
## iter 10 value 659.890443
## iter 20 value 563.409043
## iter 30 value 348.801294
## iter 40 value 290.148868
## iter 50 value 281.688208
## iter 60 value 281.465952
## iter 70 value 276.641736
## iter 80 value 225.426662
## iter 90 value 222.539671
## iter 100 value 220.810919
## final value 220.810919
## stopped after 100 iterations
## # weights: 67
## initial value 844.945264
## iter 10 value 684.363932
## iter 20 value 659.220709
## iter 30 value 658.819421
## iter 40 value 646.148585
## iter 50 value 458.699443
## iter 60 value 411.758156
## iter 70 value 329.952763
## iter 80 value 255.909228
## iter 90 value 188.539691
## iter 100 value 148.631042
## final value 148.631042
## stopped after 100 iterations
## # weights: 109
## initial value 993.468303
## iter 10 value 654.017584
## iter 20 value 636.207464
## iter 30 value 528.300413
## iter 40 value 361.683556
## iter 50 value 253.324241
## iter 60 value 208.924758
## iter 70 value 149.868566
## iter 80 value 138.479053
## iter 90 value 99.035585
## iter 100 value 88.274266
## final value 88.274266
## stopped after 100 iterations
## # weights: 151
## initial value 856.575384
## iter 10 value 647.699094
## iter 20 value 300.647900
## iter 30 value 294.425529
## iter 40 value 280.139703
## iter 50 value 275.921367
## iter 60 value 265.665717
## iter 70 value 202.310191
## iter 80 value 193.566093
## iter 90 value 164.581252
## iter 100 value 160.713848
## final value 160.713848
## stopped after 100 iterations
## # weights: 46
## initial value 981.508380
## iter 10 value 663.408478
## iter 20 value 660.785307
## iter 30 value 660.737144
## iter 40 value 637.757513
## iter 50 value 512.397313
## iter 60 value 509.814615
## iter 70 value 508.767835
## iter 80 value 368.988549
## iter 90 value 309.552581
## iter 100 value 293.947725
## final value 293.947725
## stopped after 100 iterations
## # weights: 67
## initial value 771.316373
## iter 10 value 650.347267
## iter 20 value 583.035491
## iter 30 value 420.603992
## iter 40 value 361.557860
## iter 50 value 258.650767
## iter 60 value 206.001787
## iter 70 value 166.459189
## iter 80 value 149.548267
## iter 90 value 143.732014
## iter 100 value 141.659373
## final value 141.659373
## stopped after 100 iterations
## # weights: 109
## initial value 1158.184355
## iter 10 value 659.186674
## iter 20 value 658.930322
## iter 30 value 643.995047
## iter 40 value 567.885089
## iter 50 value 358.973163
## iter 60 value 313.663443
## iter 70 value 293.343259
## iter 80 value 272.633874
## iter 90 value 255.498194
## iter 100 value 240.684671
## final value 240.684671
## stopped after 100 iterations
## # weights: 151
## initial value 1028.734103
## iter 10 value 661.923397
## iter 20 value 658.758899
## iter 30 value 658.603597
## iter 40 value 504.928801
## iter 50 value 344.712140
## iter 60 value 328.792636
## iter 70 value 285.917849
## iter 80 value 269.828373
## iter 90 value 246.019703
## iter 100 value 194.926350
## final value 194.926350
## stopped after 100 iterations
## # weights: 46
## initial value 763.699539
## final value 653.225369
## converged
## # weights: 67
## initial value 838.012597
## iter 10 value 654.991218
## iter 20 value 541.568159
## iter 30 value 401.609862
## iter 40 value 298.117248
## iter 50 value 266.473285
## iter 60 value 265.797011
## iter 70 value 265.110962
## iter 80 value 241.882884
## iter 90 value 182.898320
## iter 100 value 148.071596
## final value 148.071596
## stopped after 100 iterations
## # weights: 109
## initial value 1257.796092
## iter 10 value 652.572999
## iter 20 value 509.035273
## iter 30 value 508.714964
## iter 40 value 504.839660
## iter 50 value 318.430294
## iter 60 value 264.413525
## iter 70 value 171.266460
## iter 80 value 163.395858
## iter 90 value 146.720494
## iter 100 value 133.208939
## final value 133.208939
## stopped after 100 iterations
## # weights: 151
## initial value 828.918008
## iter 10 value 655.989143
## iter 20 value 631.892938
## iter 30 value 465.563160
## iter 40 value 361.115757
## iter 50 value 277.224146
## iter 60 value 272.741374
## iter 70 value 271.558635
## iter 80 value 236.288703
## iter 90 value 203.212748
## iter 100 value 185.324471
## final value 185.324471
## stopped after 100 iterations
## # weights: 46
## initial value 759.188248
## iter 10 value 653.684583
## iter 20 value 652.296406
## iter 30 value 542.344576
## iter 40 value 513.564029
## iter 50 value 384.074645
## iter 60 value 282.355380
## iter 70 value 276.575271
## iter 80 value 263.446295
## iter 90 value 234.478349
## iter 100 value 222.132520
## final value 222.132520
## stopped after 100 iterations
## # weights: 67
## initial value 1118.885603
## iter 10 value 659.171982
## iter 20 value 567.527620
## iter 30 value 422.470931
## iter 40 value 394.427625
## iter 50 value 355.480646
## iter 60 value 238.367271
## iter 70 value 196.141435
## iter 80 value 158.137856
## iter 90 value 133.416837
## iter 100 value 120.154678
## final value 120.154678
## stopped after 100 iterations
## # weights: 109
## initial value 802.164724
## iter 10 value 655.141020
## iter 20 value 649.616439
## iter 30 value 633.360361
## iter 40 value 388.019544
## iter 50 value 307.132048
## iter 60 value 267.765039
## iter 70 value 246.914022
## iter 80 value 238.634911
## iter 90 value 182.678495
## iter 100 value 144.952216
## final value 144.952216
## stopped after 100 iterations
## # weights: 151
## initial value 874.032104
## iter 10 value 654.861514
## iter 20 value 652.764566
## iter 30 value 652.737924
## iter 40 value 564.737189
## iter 50 value 478.174278
## iter 60 value 462.872295
## iter 70 value 440.247379
## iter 80 value 411.834541
## iter 90 value 387.822849
## iter 100 value 328.986647
## final value 328.986647
## stopped after 100 iterations
## # weights: 46
## initial value 1115.413357
## iter 10 value 663.103037
## iter 20 value 653.708330
## iter 30 value 542.214355
## iter 40 value 336.138211
## iter 50 value 297.752939
## iter 60 value 279.893162
## iter 70 value 268.083520
## iter 80 value 267.867946
## iter 90 value 266.440864
## iter 100 value 243.410843
## final value 243.410843
## stopped after 100 iterations
## # weights: 67
## initial value 811.658899
## iter 10 value 653.643135
## iter 20 value 652.990403
## iter 30 value 647.873389
## iter 40 value 547.400186
## iter 50 value 402.828254
## iter 60 value 337.334876
## iter 70 value 302.449320
## iter 80 value 253.482106
## iter 90 value 197.830914
## iter 100 value 191.410366
## final value 191.410366
## stopped after 100 iterations
## # weights: 109
## initial value 1007.591263
## iter 10 value 657.529909
## iter 20 value 639.646118
## iter 30 value 424.205720
## iter 40 value 308.469441
## iter 50 value 299.248197
## iter 60 value 241.229258
## iter 70 value 225.121634
## iter 80 value 196.732321
## iter 90 value 176.618296
## iter 100 value 167.338893
## final value 167.338893
## stopped after 100 iterations
## # weights: 151
## initial value 716.404624
## iter 10 value 652.645786
## iter 20 value 624.818298
## iter 30 value 486.714115
## iter 40 value 359.417442
## iter 50 value 313.721126
## iter 60 value 234.992126
## iter 70 value 183.050773
## iter 80 value 170.151479
## iter 90 value 155.518698
## iter 100 value 149.368703
## final value 149.368703
## stopped after 100 iterations
## # weights: 67
## initial value 1248.942473
## iter 10 value 984.008221
## iter 20 value 983.641457
## iter 30 value 887.582069
## iter 40 value 851.632627
## iter 50 value 771.355257
## iter 60 value 661.216247
## iter 70 value 596.925053
## iter 80 value 561.779733
## iter 90 value 509.101360
## iter 100 value 459.391159
## final value 459.391159
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n4_NN1Fit0
## Neural Network
##
## 894 samples
## 16 predictor
## 4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 595, 598, 595
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.7623497 0.5416868
## 2 0.5 0.8579989 0.7611538
## 2 0.7 0.8378190 0.7201753
## 3 0.3 0.8734935 0.7831060
## 3 0.5 0.9542017 0.9253396
## 3 0.7 0.9196421 0.8642356
## 5 0.3 0.9102828 0.8478730
## 5 0.5 0.9330426 0.8922783
## 5 0.7 0.9046973 0.8386499
## 7 0.3 0.9340444 0.8928238
## 7 0.5 0.9027841 0.8357405
## 7 0.7 0.9351479 0.8927513
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.5.
DryBean_TDA_PC_5.60.5_n4_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.9464883 0.9138530 Fold3
## 2 0.9729730 0.9559393 Fold2
## 3 0.9431438 0.9062264 Fold1
db_tda_pc_5.60.5_n4_nn1_fit_re<-DryBean_TDA_PC_5.60.5_n4_NN1Fit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n4_NN1Fit0)
## a 16-3-4 network with 67 weights
## options were - softmax modelling decay=0.5
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## -0.01 0.00 -0.63 -0.25 -0.49 -0.05 -0.01 0.00 2.70 0.07
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.01
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.02 0.33 0.07 -0.71 0.57 0.05 0.01 -0.30 -1.13 -0.04
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.02 0.00 0.01 0.00 0.00 0.00 0.01
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.02 -0.09 0.39 0.29 -0.04 0.06 0.02 0.08 -1.04 0.04
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.02 0.01 0.01 0.00 0.00 0.01 0.02
## b->o1 h1->o1 h2->o1 h3->o1
## -0.76 -3.62 1.88 -0.08
## b->o2 h1->o2 h2->o2 h3->o2
## 0.81 3.71 -1.00 -2.45
## b->o3 h1->o3 h2->o3 h3->o3
## 0.00 3.00 -1.41 1.15
## b->o4 h1->o4 h2->o4 h3->o4
## -0.05 -3.08 0.53 1.38
#vip(DryBean_TDA_PC_5.60.5_n4_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.60.5_n4_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_PC_5.60.5_n4_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n4_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n4_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n4_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 130 6 0 0 2 0
## CALI 210 26 470 1027 98 590 771
## DERMASON 0 0 0 0 0 0 0
## HOROZ 186 0 13 36 480 16 19
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.2647
## 95% CI : (0.2512, 0.2785)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 0.2775
##
## Kappa : 0.1634
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.83333 0.9611 0.0000
## Specificity 1.00000 0.99796 0.2420 1.0000
## Pos Pred Value NaN 0.94203 0.1472 NaN
## Neg Pred Value 0.90294 0.99340 0.9786 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.03186 0.1152 0.0000
## Detection Prevalence 0.00000 0.03382 0.7824 0.0000
## Balanced Accuracy 0.50000 0.91565 0.6016 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8304 0.000 0.0000
## Specificity 0.9229 1.000 1.0000
## Pos Pred Value 0.6400 NaN NaN
## Neg Pred Value 0.9706 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1176 0.000 0.0000
## Detection Prevalence 0.1838 0.000 0.0000
## Balanced Accuracy 0.8767 0.500 0.5000
db_tda_pc_5.60.5_n4_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 130 6 0 0 2 0
## CALI 210 26 470 1027 98 590 771
## DERMASON 0 0 0 0 0 0 0
## HOROZ 186 0 13 36 480 16 19
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.2647
## 95% CI : (0.2512, 0.2785)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 0.2775
##
## Kappa : 0.1634
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.83333 0.9611 0.0000
## Specificity 1.00000 0.99796 0.2420 1.0000
## Pos Pred Value NaN 0.94203 0.1472 NaN
## Neg Pred Value 0.90294 0.99340 0.9786 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.03186 0.1152 0.0000
## Detection Prevalence 0.00000 0.03382 0.7824 0.0000
## Balanced Accuracy 0.50000 0.91565 0.6016 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8304 0.000 0.0000
## Specificity 0.9229 1.000 1.0000
## Pos Pred Value 0.6400 NaN NaN
## Neg Pred Value 0.9706 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1176 0.000 0.0000
## Detection Prevalence 0.1838 0.000 0.0000
## Balanced Accuracy 0.8767 0.500 0.5000
db_tda_pc_5.60.5_n4_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.2647059 0.1633906 0.2512184 0.2785310 0.2605392
## AccuracyPValue McnemarPValue
## 0.2774710 NaN
db_tda_pc_5.60.5_n4_db_nn1_cf0_ov_acc<-db_tda_pc_5.60.5_n4_db_nn1_cf0$overall[1]
db_tda_pc_5.60.5_n4_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.8333333 0.9979613 0.9420290 0.9934044 0.9420290
## Class: CALI 0.9611452 0.2419939 0.1472431 0.9786036 0.1472431
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.8304498 0.9229012 0.6400000 0.9705706 0.6400000
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.00000000
## Class: BOMBAY 0.8333333 0.8843537 0.03823529 0.03186275
## Class: CALI 0.9611452 0.2553654 0.11985294 0.11519608
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.8304498 0.7228916 0.14166667 0.11764706
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.00000000 0.5000000
## Class: BOMBAY 0.03382353 0.9156473
## Class: CALI 0.78235294 0.6015695
## Class: DERMASON 0.00000000 0.5000000
## Class: HOROZ 0.18382353 0.8766755
## Class: SEKER 0.00000000 0.5000000
## Class: SIRA 0.00000000 0.5000000
db_tda_pc_5.60.5_n4_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n4_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold<-(db_nn1_fit_re - db_tda_pc_5.60.5_n4_nn1_fit_re)
diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold
## Accuracy
## 1 -0.4721414
## 2 -0.2335962
## 3 -0.6828353
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold
## $winLeft
## [1] 0.9909333
##
## $winRope
## [1] 0.009066667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n4_3_fold
## $left
## [1] 0.9528708
##
## $rope
## [1] 0.003406066
##
## $right
## [1] 0.04372312
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold))
#bf_tda_pca_5.60.5_nn1.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n4_3_fold)
## t = -3.5668, df = 2, p-value = 0.0704
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.02120094 0.09548562
## sample estimates:
## mean of x
## -0.4628577
### Test set diff
diff_drybean_tda_pca_5.60.5_nn1.n4_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.60.5_n4_db_nn1_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nn1.n4_test
## Accuracy
## 0.1132353
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nn1.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nn1.n4_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n4_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n4_test$probRight
bst_dbf_db_tda_pca_5.60.5_nn1.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nn1.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nn1.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1556
##
## $winRight
## [1] 0.8444
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nn1.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nn1.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nn1.n4_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n4_test)) #bf_tda_pca_5.60.5_nn1.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n4_test))
##Node5
#Neural Network 1
#DryBean_TDA_PC_5.60.5_n5_NN1Fit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.60.5.n5.vec,
# Importance = T,
# method = 'nnet',
# trControl = fitControl,
# tuneGrid = nn1Grid,
# metric='Accuracy')
#DryBean_TDA_PC_5.60.5_n5_NN1Fit0
#DryBean_TDA_PC_5.60.5_n5_NN1Fit0$resample
#db_tda_pc_5.60.5_n5_nn1_fit_re<-DryBean_TDA_PC_5.60.5_n5_NN1Fit0$resample[1]
#summary(DryBean_TDA_PC_5.60.5_n5_NN1Fit0)
#vip(DryBean_TDA_PC_5.60.5_n5_NN1Fit0,25) + ggtitle("dryBean_TDA_PCA_5.60.5_n5_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_PC_5.60.5_n5_NN1Fit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.60.5_n5_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.60.5_n5_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.60.5_n5_db_nn1_cf0
#db_tda_pc_5.60.5_n5_db_nn1_cf0
#db_tda_pc_5.60.5_n5_db_nn1_cf0$overall
#db_tda_pc_5.60.5_n5_db_nn1_cf0_ov_acc<-db_tda_pc_5.60.5_n5_db_nn1_cf0$overall[1]
#db_tda_pc_5.60.5_n5_db_nn1_cf0$byClass
#db_tda_pc_5.60.5_n5_db_nn1_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n5_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold<-(db_nn1_fit_re - db_tda_pc_5.60.5_n5_nn1_fit_re)
#diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold#
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_nn1.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold))
#bf_tda_pca_5.60.5_nn1.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1_n5_3_fold))
### Test set diff
#diff_drybean_tda_pca_5.60.5_nn1.n5_test<-(db_nn1_cf_ov_acc - db_tda_pc_5.60.5_n5_db_nn1_cf0_ov_acc)
#diff_drybean_tda_pca_5.60.5_nn1.n5_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_test
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nn1.n5_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nn1.n5_test$probRight
#bst_dbf_db_tda_pca_5.60.5_nn1.n5_test_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_nn1.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_nn1.n5_test
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_nn1.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_nn1.n5_test
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nn1.n5_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nn1.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n5_test)) #bf_tda_pca_5.60.5_nn1.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nn1.n5_test))
##With TDA KDE filter 5 intervals, 50% overlap, 5 bins
##Node1
#Neural Network 1
DryBean_TDA_KDE_5.60.5_n1_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n1.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 55
## initial value 10585.562703
## iter 10 value 9243.833776
## iter 20 value 9177.635965
## iter 30 value 8417.773326
## iter 40 value 8195.514763
## iter 50 value 7951.440958
## iter 60 value 7055.157469
## iter 70 value 6127.570515
## iter 80 value 5739.581681
## iter 90 value 5672.221809
## iter 100 value 5420.015591
## final value 5420.015591
## stopped after 100 iterations
## # weights: 79
## initial value 10199.465953
## iter 10 value 9243.953741
## iter 20 value 7771.385814
## iter 30 value 7370.509658
## iter 40 value 7308.863084
## iter 50 value 7243.977408
## iter 60 value 7191.245760
## iter 70 value 6181.412428
## iter 80 value 5472.108244
## iter 90 value 5187.068724
## iter 100 value 4931.517621
## final value 4931.517621
## stopped after 100 iterations
## # weights: 127
## initial value 12781.211482
## iter 10 value 9273.271830
## iter 20 value 9239.971477
## iter 30 value 9184.556374
## iter 40 value 9166.290471
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## iter 70 value 7673.103893
## iter 80 value 6358.522241
## iter 90 value 6206.225107
## iter 100 value 6186.159157
## final value 6186.159157
## stopped after 100 iterations
## # weights: 175
## initial value 11536.263183
## iter 10 value 9243.591291
## iter 20 value 8881.827629
## iter 30 value 7343.135497
## iter 40 value 7291.948877
## iter 50 value 7267.557266
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## iter 70 value 7234.278791
## iter 80 value 7202.984449
## iter 90 value 6987.623739
## iter 100 value 6821.632470
## final value 6821.632470
## stopped after 100 iterations
## # weights: 55
## initial value 10278.310818
## iter 10 value 9331.408271
## iter 20 value 9246.681862
## iter 30 value 9244.746387
## iter 40 value 9244.448467
## iter 50 value 9243.904020
## iter 50 value 9243.903962
## iter 60 value 9240.407558
## iter 70 value 9044.303588
## iter 80 value 8430.171911
## iter 90 value 7877.381308
## iter 100 value 7435.571073
## final value 7435.571073
## stopped after 100 iterations
## # weights: 79
## initial value 10830.721881
## iter 10 value 9247.626938
## iter 20 value 9243.780273
## iter 30 value 9236.315342
## iter 40 value 7798.363363
## iter 50 value 7173.540952
## iter 60 value 6387.116180
## iter 70 value 5979.092516
## iter 80 value 5842.617630
## iter 90 value 5793.231485
## iter 100 value 5748.754883
## final value 5748.754883
## stopped after 100 iterations
## # weights: 127
## initial value 9977.561327
## iter 10 value 9254.136735
## iter 20 value 9243.859238
## iter 30 value 9243.735575
## iter 40 value 9145.240083
## iter 50 value 8118.779482
## iter 60 value 8068.079122
## iter 70 value 8009.232937
## iter 80 value 7352.553402
## iter 90 value 6344.220927
## iter 100 value 5999.928308
## final value 5999.928308
## stopped after 100 iterations
## # weights: 175
## initial value 10438.478338
## iter 10 value 9260.587188
## iter 20 value 9243.960745
## final value 9243.741333
## converged
## # weights: 55
## initial value 10753.627629
## iter 10 value 9244.287717
## final value 9244.022196
## converged
## # weights: 79
## initial value 10301.792744
## iter 10 value 9246.632686
## iter 20 value 9244.437539
## iter 30 value 9239.437812
## iter 40 value 8472.823082
## iter 50 value 8119.299548
## iter 60 value 7326.935494
## iter 70 value 6226.239079
## iter 80 value 5859.888646
## iter 90 value 5780.875178
## iter 100 value 5488.992267
## final value 5488.992267
## stopped after 100 iterations
## # weights: 127
## initial value 10325.860926
## iter 10 value 9243.708253
## iter 20 value 8161.406316
## iter 30 value 8011.060747
## iter 40 value 7414.892967
## iter 50 value 7214.446555
## iter 60 value 6919.544039
## iter 70 value 6504.412273
## iter 80 value 5208.458192
## iter 90 value 4554.375641
## iter 100 value 4295.671482
## final value 4295.671482
## stopped after 100 iterations
## # weights: 175
## initial value 10481.231429
## iter 10 value 9243.950058
## iter 20 value 9243.235180
## iter 30 value 7878.243903
## iter 40 value 7585.861855
## iter 50 value 7449.356600
## iter 60 value 7165.466169
## iter 70 value 7064.501119
## iter 80 value 6646.431719
## iter 90 value 6185.506379
## iter 100 value 5958.743628
## final value 5958.743628
## stopped after 100 iterations
## # weights: 55
## initial value 9997.039550
## iter 10 value 9242.516244
## iter 20 value 9237.691014
## iter 30 value 7893.925411
## iter 40 value 7453.661930
## iter 50 value 7238.923286
## iter 60 value 7125.132212
## iter 70 value 6874.524173
## iter 80 value 6657.633681
## iter 90 value 6614.592917
## iter 100 value 6471.195515
## final value 6471.195515
## stopped after 100 iterations
## # weights: 79
## initial value 10143.553731
## iter 10 value 9295.111868
## iter 20 value 9236.909930
## iter 30 value 8840.476729
## iter 40 value 8705.477320
## iter 50 value 8421.848559
## iter 60 value 7505.702885
## iter 70 value 6606.201702
## iter 80 value 6398.487098
## iter 90 value 6359.300043
## iter 100 value 6299.480150
## final value 6299.480150
## stopped after 100 iterations
## # weights: 127
## initial value 11074.180260
## iter 10 value 9729.610440
## iter 20 value 9712.099399
## iter 30 value 8169.957934
## iter 40 value 7571.659338
## iter 50 value 7448.593265
## iter 60 value 7283.380214
## iter 70 value 6947.880512
## iter 80 value 6815.772773
## iter 90 value 6650.489040
## iter 100 value 6584.255823
## final value 6584.255823
## stopped after 100 iterations
## # weights: 175
## initial value 10373.654416
## iter 10 value 9242.703839
## iter 20 value 9231.504684
## iter 30 value 8558.878111
## iter 40 value 8166.859172
## iter 50 value 7896.385501
## iter 60 value 7304.853365
## iter 70 value 6985.140342
## iter 80 value 6914.284414
## iter 90 value 6316.429700
## iter 100 value 6181.543758
## final value 6181.543758
## stopped after 100 iterations
## # weights: 55
## initial value 9655.754509
## iter 10 value 9246.011303
## iter 20 value 9242.664546
## iter 30 value 9237.031029
## iter 40 value 8392.445580
## iter 50 value 7896.700913
## iter 60 value 6833.088390
## iter 70 value 6729.197016
## iter 80 value 6705.575260
## iter 90 value 6702.284688
## iter 100 value 6571.489269
## final value 6571.489269
## stopped after 100 iterations
## # weights: 79
## initial value 10988.201516
## iter 10 value 9551.362590
## iter 20 value 9262.593520
## iter 30 value 9245.956189
## iter 40 value 9037.438916
## iter 50 value 8446.929901
## iter 60 value 8158.526849
## iter 70 value 8083.610592
## iter 80 value 8058.912832
## iter 90 value 8043.583691
## iter 100 value 8040.777267
## final value 8040.777267
## stopped after 100 iterations
## # weights: 127
## initial value 11098.828800
## iter 10 value 9244.383226
## iter 20 value 9242.449476
## iter 30 value 9241.650871
## iter 40 value 7766.903580
## iter 50 value 7283.094254
## iter 60 value 7163.709914
## iter 70 value 6428.563790
## iter 80 value 6019.318329
## iter 90 value 5459.395190
## iter 100 value 5216.632015
## final value 5216.632015
## stopped after 100 iterations
## # weights: 175
## initial value 9877.962213
## iter 10 value 9247.724592
## iter 20 value 9242.682541
## iter 30 value 9233.694736
## iter 40 value 9055.988616
## iter 50 value 8455.595054
## iter 60 value 8289.303515
## iter 70 value 8140.786070
## iter 80 value 7685.198092
## iter 90 value 7036.096013
## iter 100 value 6598.731366
## final value 6598.731366
## stopped after 100 iterations
## # weights: 55
## initial value 9846.851744
## iter 10 value 9243.343407
## iter 20 value 9242.616161
## final value 9242.612871
## converged
## # weights: 79
## initial value 10238.125411
## iter 10 value 9242.683575
## final value 9242.597104
## converged
## # weights: 127
## initial value 10553.977082
## iter 10 value 9243.193199
## iter 20 value 9242.461374
## iter 30 value 7873.407130
## iter 40 value 7598.961539
## iter 50 value 7434.776865
## iter 60 value 7393.204913
## iter 70 value 7376.256800
## iter 80 value 6905.352189
## iter 90 value 6005.114298
## iter 100 value 5629.520460
## final value 5629.520460
## stopped after 100 iterations
## # weights: 175
## initial value 11757.991493
## iter 10 value 9287.365494
## iter 20 value 7810.015892
## iter 30 value 7639.139465
## iter 40 value 7415.397997
## iter 50 value 7372.672693
## iter 60 value 7350.647648
## iter 70 value 7167.016826
## iter 80 value 5784.832115
## iter 90 value 5492.443641
## iter 100 value 4563.168568
## final value 4563.168568
## stopped after 100 iterations
## # weights: 55
## initial value 9632.411203
## iter 10 value 9246.690873
## iter 20 value 9246.506391
## iter 30 value 8515.381129
## iter 40 value 8461.409343
## iter 50 value 8365.546042
## iter 60 value 8259.839886
## iter 70 value 7232.335429
## iter 80 value 6928.645171
## iter 90 value 6836.297113
## iter 100 value 6834.781619
## final value 6834.781619
## stopped after 100 iterations
## # weights: 79
## initial value 11031.357429
## iter 10 value 9246.373850
## iter 20 value 9234.742851
## iter 30 value 7280.645479
## iter 40 value 7096.070497
## iter 50 value 6933.489205
## iter 60 value 6207.672540
## iter 70 value 6119.151877
## iter 80 value 6064.528328
## iter 90 value 6044.990752
## iter 100 value 6019.320393
## final value 6019.320393
## stopped after 100 iterations
## # weights: 127
## initial value 9957.863803
## iter 10 value 9246.694089
## final value 9246.687773
## converged
## # weights: 175
## initial value 10868.449178
## iter 10 value 9246.347433
## iter 20 value 9237.889125
## iter 30 value 7533.043731
## iter 40 value 7355.839400
## iter 50 value 7216.139951
## iter 60 value 7086.110085
## iter 70 value 6950.454010
## iter 80 value 6793.206472
## iter 90 value 6698.339645
## iter 100 value 6143.653061
## final value 6143.653061
## stopped after 100 iterations
## # weights: 55
## initial value 10090.729029
## iter 10 value 9274.999344
## iter 20 value 9247.662778
## iter 30 value 9247.470721
## iter 40 value 9194.737756
## iter 50 value 8916.004287
## iter 60 value 8588.974671
## iter 70 value 8078.729412
## iter 80 value 7750.203784
## iter 90 value 7470.402486
## iter 100 value 7352.743088
## final value 7352.743088
## stopped after 100 iterations
## # weights: 79
## initial value 9735.088142
## iter 10 value 9250.384803
## iter 20 value 9233.976893
## iter 30 value 8407.707227
## iter 40 value 8250.677052
## iter 50 value 8166.611122
## iter 60 value 8130.682135
## iter 70 value 8054.213316
## iter 80 value 7638.227283
## iter 90 value 7218.854261
## iter 100 value 7157.121078
## final value 7157.121078
## stopped after 100 iterations
## # weights: 127
## initial value 9997.784797
## iter 10 value 9252.166159
## iter 20 value 9246.542869
## iter 30 value 9053.191469
## iter 40 value 8537.298796
## iter 50 value 7993.752171
## iter 60 value 6837.222256
## iter 70 value 6372.262642
## iter 80 value 5064.475560
## iter 90 value 4721.399528
## iter 100 value 4396.882132
## final value 4396.882132
## stopped after 100 iterations
## # weights: 175
## initial value 11019.791150
## iter 10 value 9279.337140
## iter 20 value 9029.655322
## iter 30 value 8658.410824
## iter 40 value 8042.662948
## iter 50 value 7357.767103
## iter 60 value 7032.694151
## iter 70 value 6854.233333
## iter 80 value 6194.189810
## iter 90 value 5289.319460
## iter 100 value 5031.060157
## final value 5031.060157
## stopped after 100 iterations
## # weights: 55
## initial value 9933.402353
## iter 10 value 9288.536087
## iter 20 value 9246.496959
## iter 30 value 8859.519710
## iter 40 value 7422.556217
## iter 50 value 6497.292165
## iter 60 value 5616.244138
## iter 70 value 5103.663946
## iter 80 value 5074.517220
## iter 90 value 4945.422026
## iter 100 value 4807.145476
## final value 4807.145476
## stopped after 100 iterations
## # weights: 79
## initial value 10992.467583
## iter 10 value 9248.381543
## iter 20 value 8963.710467
## iter 30 value 8930.758283
## iter 40 value 8160.452387
## iter 50 value 7527.220755
## iter 60 value 7434.798100
## iter 70 value 7055.202016
## iter 80 value 7009.615271
## iter 90 value 6488.774002
## iter 100 value 6096.890983
## final value 6096.890983
## stopped after 100 iterations
## # weights: 127
## initial value 10659.007608
## iter 10 value 9247.286777
## iter 20 value 9222.142116
## iter 30 value 8324.416755
## iter 40 value 8247.352341
## iter 50 value 8170.534585
## iter 60 value 7862.378072
## iter 70 value 7703.649611
## iter 80 value 7373.939389
## iter 90 value 7230.730871
## iter 100 value 7156.099129
## final value 7156.099129
## stopped after 100 iterations
## # weights: 175
## initial value 10946.170284
## iter 10 value 9255.736285
## iter 20 value 9237.456698
## iter 30 value 7349.340531
## iter 40 value 6661.022000
## iter 50 value 6549.771586
## iter 60 value 6358.193306
## iter 70 value 6099.206341
## iter 80 value 5861.954847
## iter 90 value 5577.885549
## iter 100 value 4885.492001
## final value 4885.492001
## stopped after 100 iterations
## # weights: 175
## initial value 17348.511101
## iter 10 value 14254.293286
## iter 20 value 13867.066144
## iter 30 value 13866.894387
## iter 40 value 13866.346492
## iter 40 value 13866.346418
## iter 50 value 13324.448686
## iter 60 value 12931.989302
## iter 70 value 12627.876565
## iter 80 value 12477.263992
## iter 90 value 12369.966460
## iter 100 value 12106.672708
## final value 12106.672708
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n1_NN1Fit0
## Neural Network
##
## 7503 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5002, 5001, 5003
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.4163717 0.2682725
## 2 0.5 0.4028968 0.2308271
## 2 0.7 0.3845748 0.2061201
## 3 0.3 0.5143307 0.3963713
## 3 0.5 0.3658739 0.2025411
## 3 0.7 0.4497387 0.2990666
## 5 0.3 0.3770220 0.2014786
## 5 0.5 0.5835155 0.4761170
## 5 0.7 0.5518963 0.4381297
## 7 0.3 0.4723539 0.3505523
## 7 0.5 0.4602526 0.3070849
## 7 0.7 0.6145450 0.5225429
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_KDE_5.60.5_n1_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.6618705 0.5844715 Fold2
## 2 0.5885646 0.4932839 Fold1
## 3 0.5932000 0.4898732 Fold3
nb_tda_kde_5.60.5_n1_nn1_fit_re<-DryBean_TDA_KDE_5.60.5_n1_NN1Fit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n1_NN1Fit0)
## a 16-7-7 network with 175 weights
## options were - softmax modelling decay=0.7
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## -0.01 0.05 1.18 -1.83 -1.82 -0.02 0.00 -0.04 -1.69 0.02
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## -0.02 -0.03 -0.02 0.00 0.00 -0.02 -0.02
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.00 -0.19 0.00 0.00 0.00 0.00 0.00 -0.19 0.00 0.00
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.00 0.06 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4 i9->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5 i9->h5
## 0.00 -0.02 0.00 0.00 0.00 0.00 0.00 -0.02 0.00 0.00
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6 i9->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7 i9->h7
## 0.00 0.79 0.02 0.01 0.00 0.00 0.00 0.80 0.01 0.00
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1
## 0.22 0.24 0.00 0.19 0.22 -0.03 0.16 0.18
## b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2
## -0.96 6.15 0.00 -0.89 -0.96 0.08 -0.80 -0.83
## b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3
## 0.09 1.97 0.00 0.05 0.09 0.01 0.05 0.07
## b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4
## 0.24 -5.70 0.01 0.23 0.24 0.04 0.27 0.22
## b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5
## 0.32 -1.09 0.00 0.26 0.32 0.02 0.27 0.26
## b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6
## 0.09 -0.76 0.00 0.09 0.09 0.00 0.09 0.09
## b->o7 h1->o7 h2->o7 h3->o7 h4->o7 h5->o7 h6->o7 h7->o7
## 0.00 -0.80 0.00 0.06 0.00 -0.10 -0.04 0.01
#vip(DryBean_TDA_KDE_5.60.5_n1_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n1_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n1_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n1_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n1_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
nb_tda_kde_5.60.5_n1_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 324 156 329 1 67 8 2
## DERMASON 0 0 0 0 0 0 0
## HOROZ 72 0 160 1062 511 600 788
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.2059
## 95% CI : (0.1936, 0.2186)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0799
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.67280 0.0000
## Specificity 1.00000 1.00000 0.84461 1.0000
## Pos Pred Value NaN NaN 0.37091 NaN
## Neg Pred Value 0.90294 0.96176 0.94989 0.7395
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.00000 0.00000 0.08064 0.0000
## Detection Prevalence 0.00000 0.00000 0.21740 0.0000
## Balanced Accuracy 0.50000 0.50000 0.75871 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8841 0.000 0.0000
## Specificity 0.2342 1.000 1.0000
## Pos Pred Value 0.1600 NaN NaN
## Neg Pred Value 0.9245 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1252 0.000 0.0000
## Detection Prevalence 0.7826 0.000 0.0000
## Balanced Accuracy 0.5591 0.500 0.5000
nb_tda_kde_5.60.5_n1_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 324 156 329 1 67 8 2
## DERMASON 0 0 0 0 0 0 0
## HOROZ 72 0 160 1062 511 600 788
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.2059
## 95% CI : (0.1936, 0.2186)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : 1
##
## Kappa : 0.0799
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.67280 0.0000
## Specificity 1.00000 1.00000 0.84461 1.0000
## Pos Pred Value NaN NaN 0.37091 NaN
## Neg Pred Value 0.90294 0.96176 0.94989 0.7395
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.00000 0.00000 0.08064 0.0000
## Detection Prevalence 0.00000 0.00000 0.21740 0.0000
## Balanced Accuracy 0.50000 0.50000 0.75871 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8841 0.000 0.0000
## Specificity 0.2342 1.000 1.0000
## Pos Pred Value 0.1600 NaN NaN
## Neg Pred Value 0.9245 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1252 0.000 0.0000
## Detection Prevalence 0.7826 0.000 0.0000
## Balanced Accuracy 0.5591 0.500 0.5000
nb_tda_kde_5.60.5_n1_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.20588235 0.07989801 0.19356672 0.21862056 0.26053922
## AccuracyPValue McnemarPValue
## 1.00000000 NaN
nb_tda_kde_5.60.5_n1_db_nn1_cf0_ov_acc<-nb_tda_kde_5.60.5_n1_db_nn1_cf0$overall[1]
nb_tda_kde_5.60.5_n1_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.6728016 0.8446115 0.3709132 0.9498904 0.3709132
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.8840830 0.2341519 0.1600376 0.9244645 0.1600376
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.00000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.6728016 0.4781977 0.11985294 0.08063725
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.8840830 0.2710156 0.14166667 0.12524510
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.000000 0.5000000
## Class: BOMBAY 0.000000 0.5000000
## Class: CALI 0.217402 0.7587066
## Class: DERMASON 0.000000 0.5000000
## Class: HOROZ 0.782598 0.5591175
## Class: SEKER 0.000000 0.5000000
## Class: SIRA 0.000000 0.5000000
nb_tda_kde_5.60.5_n1_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n1_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.60.5_n1_nn1_fit_re)
diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold
## Accuracy
## 1 -0.1875236
## 2 0.1508122
## 3 -0.3328915
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n1_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n1_3_fold_odds.left<-bst_tda_kde_5.60.5_nn1.n1_3_fold$probLeft/bst_tda_kde_5.60.5_nn1.n1_3_fold$probRight
bst_tda_kde_5.60.5_nn1.n1_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n1_3_fold
## $winLeft
## [1] 0.875
##
## $winRope
## [1] 0.0165
##
## $winRight
## [1] 0.1085
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n1_3_fold
## $left
## [1] 0.7177491
##
## $rope
## [1] 0.02960923
##
## $right
## [1] 0.2526416
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold))
#bf_tda_kde_5.60.5_nn1.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n1_3_fold)
## t = -0.85981, df = 2, p-value = 0.4805
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7397246 0.4933226
## sample estimates:
## mean of x
## -0.123201
### Test set diff
diff_drybean_tda_kde_5.60.5_nn1.n1_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.60.5_n1_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nn1.n1_test
## Accuracy
## 0.1720588
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n1_test),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n1_test_odds.left<-bst_tda_kde_5.60.5_nn1.n1_test$probLeft/bst_tda_kde_5.60.5_nn1.n1_test$probRight
bst_tda_kde_5.60.5_nn1.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n1_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1578667
##
## $winRight
## [1] 0.8421333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nn1.n1_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n1_test)) #bf_tda_pca_5.60.5_nn1.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n1_test))
##Node2
#Neural Network 1
DryBean_TDA_KDE_5.60.5_n2_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n2.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 52
## initial value 9556.781358
## iter 10 value 7573.789388
## iter 20 value 7573.583511
## final value 7573.482997
## converged
## # weights: 75
## initial value 8339.854982
## iter 10 value 7574.762732
## iter 20 value 7573.498252
## final value 7573.482918
## converged
## # weights: 121
## initial value 9015.216627
## iter 10 value 7573.416628
## final value 7573.414345
## converged
## # weights: 167
## initial value 9940.688748
## iter 10 value 7573.414792
## final value 7573.414377
## converged
## # weights: 52
## initial value 9599.044861
## iter 10 value 7589.933951
## iter 20 value 7573.924805
## iter 30 value 7573.669114
## final value 7573.665999
## converged
## # weights: 75
## initial value 9673.522070
## iter 10 value 7584.239061
## iter 20 value 7574.022570
## iter 30 value 7573.894395
## iter 40 value 7573.692636
## iter 50 value 7291.468579
## iter 60 value 5835.096445
## iter 70 value 5508.070449
## iter 80 value 5426.961330
## iter 90 value 5403.300342
## iter 100 value 4839.528939
## final value 4839.528939
## stopped after 100 iterations
## # weights: 121
## initial value 10424.520348
## iter 10 value 7578.547221
## iter 20 value 7573.612182
## iter 30 value 7573.552333
## final value 7573.551668
## converged
## # weights: 167
## initial value 8495.576497
## iter 10 value 7573.660551
## iter 20 value 7573.552887
## final value 7573.551696
## converged
## # weights: 52
## initial value 8359.838172
## iter 10 value 7575.852985
## iter 20 value 7574.986304
## iter 30 value 7574.179146
## iter 40 value 7574.125980
## iter 50 value 7573.872978
## iter 60 value 7573.849014
## iter 60 value 7573.849008
## iter 60 value 7573.849008
## final value 7573.849008
## converged
## # weights: 75
## initial value 8057.487920
## iter 10 value 7574.198950
## iter 20 value 7574.018651
## iter 30 value 7573.868880
## final value 7573.848943
## converged
## # weights: 121
## initial value 8606.371392
## iter 10 value 7574.279313
## iter 20 value 7497.681301
## iter 30 value 5734.564040
## iter 40 value 5330.699721
## iter 50 value 5111.615923
## iter 60 value 4481.404960
## iter 70 value 4105.984439
## iter 80 value 3882.439212
## iter 90 value 3479.626300
## iter 100 value 3122.035117
## final value 3122.035117
## stopped after 100 iterations
## # weights: 167
## initial value 8135.149955
## iter 10 value 7574.072220
## iter 20 value 7481.976886
## iter 30 value 6845.032506
## iter 40 value 6026.184716
## iter 50 value 5830.409246
## iter 60 value 5315.793911
## iter 70 value 5199.638817
## iter 80 value 5183.975322
## iter 90 value 4316.184972
## iter 100 value 3690.526958
## final value 3690.526958
## stopped after 100 iterations
## # weights: 52
## initial value 8361.708636
## iter 10 value 7577.438244
## final value 7577.437661
## converged
## # weights: 75
## initial value 9523.845683
## iter 10 value 7577.438402
## final value 7577.437545
## converged
## # weights: 121
## initial value 9290.718843
## iter 10 value 7577.437935
## final value 7577.437371
## converged
## # weights: 167
## initial value 9920.635228
## iter 10 value 7580.844837
## iter 20 value 7147.678118
## iter 30 value 5928.926139
## iter 40 value 5548.493623
## iter 50 value 5437.910917
## iter 60 value 5366.505843
## iter 70 value 5297.347841
## iter 80 value 5092.078349
## iter 90 value 4759.767661
## iter 100 value 3200.279159
## final value 3200.279159
## stopped after 100 iterations
## # weights: 52
## initial value 8427.014439
## iter 10 value 7608.469056
## iter 20 value 7578.236314
## iter 30 value 7577.853583
## iter 40 value 7577.848255
## iter 50 value 7577.631484
## final value 7577.620556
## converged
## # weights: 75
## initial value 8336.287993
## iter 10 value 7596.190269
## iter 20 value 7578.089034
## iter 30 value 7577.847612
## iter 40 value 7207.131607
## iter 50 value 5884.168128
## iter 60 value 5674.967996
## iter 70 value 5153.836845
## iter 80 value 4822.132169
## iter 90 value 4460.730048
## iter 100 value 4386.886799
## final value 4386.886799
## stopped after 100 iterations
## # weights: 121
## initial value 8481.909938
## iter 10 value 7599.324993
## iter 20 value 7577.871340
## iter 30 value 7577.510636
## iter 40 value 7339.804173
## iter 50 value 5721.229277
## iter 60 value 5504.708165
## iter 70 value 5033.741019
## iter 80 value 4904.214448
## iter 90 value 4873.066486
## iter 100 value 4653.404491
## final value 4653.404491
## stopped after 100 iterations
## # weights: 167
## initial value 9954.042270
## iter 10 value 7624.152521
## iter 20 value 7577.811892
## iter 30 value 7577.397416
## iter 40 value 7574.193965
## iter 50 value 7293.913770
## iter 60 value 6224.812074
## iter 70 value 5976.554489
## iter 80 value 5508.402360
## iter 90 value 5365.502290
## iter 100 value 4695.351057
## final value 4695.351057
## stopped after 100 iterations
## # weights: 52
## initial value 11083.285365
## iter 10 value 7595.864320
## iter 20 value 7578.092260
## iter 30 value 7562.912361
## iter 40 value 6259.383965
## iter 50 value 6034.802583
## iter 60 value 5484.733308
## iter 70 value 4889.646204
## iter 80 value 4640.773950
## iter 90 value 4514.903554
## iter 100 value 4074.408384
## final value 4074.408384
## stopped after 100 iterations
## # weights: 75
## initial value 8312.643396
## iter 10 value 7578.125050
## final value 7578.123035
## converged
## # weights: 121
## initial value 10246.909165
## iter 10 value 7578.348126
## iter 20 value 7533.840635
## iter 30 value 5259.852665
## iter 40 value 4462.787497
## iter 50 value 4179.408075
## iter 60 value 4072.341237
## iter 70 value 3972.135277
## iter 80 value 3911.356309
## iter 90 value 3860.095045
## iter 100 value 3782.379525
## final value 3782.379525
## stopped after 100 iterations
## # weights: 167
## initial value 8391.634950
## iter 10 value 7581.409121
## iter 20 value 7577.846124
## iter 30 value 7573.738275
## iter 40 value 5678.103310
## iter 50 value 5529.373631
## iter 60 value 5421.949890
## iter 70 value 4414.883964
## iter 80 value 3688.459266
## iter 90 value 3469.462657
## iter 100 value 3274.634921
## final value 3274.634921
## stopped after 100 iterations
## # weights: 52
## initial value 10176.783199
## iter 10 value 7630.882494
## iter 20 value 7579.194773
## iter 30 value 7425.501898
## iter 40 value 6462.963334
## iter 50 value 5190.476296
## iter 60 value 5068.064649
## iter 70 value 4982.291039
## iter 80 value 4875.190145
## iter 90 value 4777.189231
## iter 100 value 4392.416494
## final value 4392.416494
## stopped after 100 iterations
## # weights: 75
## initial value 10486.662558
## iter 10 value 7577.410261
## final value 7577.326381
## converged
## # weights: 121
## initial value 11243.195055
## iter 10 value 7577.704691
## iter 20 value 7478.079134
## iter 30 value 7359.653245
## iter 40 value 6399.398395
## iter 50 value 5249.442674
## iter 60 value 5061.051891
## iter 70 value 4950.410786
## iter 80 value 4933.146613
## iter 90 value 4889.404128
## iter 100 value 4825.226273
## final value 4825.226273
## stopped after 100 iterations
## # weights: 167
## initial value 8444.932446
## iter 10 value 7577.226124
## final value 7577.217012
## converged
## # weights: 52
## initial value 8578.152330
## iter 10 value 7584.314269
## iter 20 value 7577.453449
## iter 30 value 6715.796913
## iter 40 value 6447.098982
## iter 50 value 6137.718846
## iter 60 value 5602.523860
## iter 70 value 5463.967124
## iter 80 value 5325.060374
## iter 90 value 5083.644334
## iter 100 value 4914.696341
## final value 4914.696341
## stopped after 100 iterations
## # weights: 75
## initial value 9726.904780
## iter 10 value 7760.237956
## iter 20 value 7580.748892
## iter 30 value 7579.370825
## iter 40 value 7577.573198
## iter 50 value 7577.514162
## iter 50 value 7577.514153
## iter 50 value 7577.514145
## final value 7577.514145
## converged
## # weights: 121
## initial value 8410.181128
## iter 10 value 7612.242835
## iter 20 value 7493.766579
## iter 30 value 6457.768132
## iter 40 value 6193.939990
## iter 50 value 5921.416187
## iter 60 value 5680.438962
## iter 70 value 5675.708544
## iter 80 value 5673.388429
## iter 90 value 5165.482997
## iter 100 value 3908.868868
## final value 3908.868868
## stopped after 100 iterations
## # weights: 167
## initial value 10466.306346
## iter 10 value 7602.828743
## iter 20 value 7577.562813
## iter 30 value 7577.283831
## final value 7577.280600
## converged
## # weights: 52
## initial value 9995.661126
## iter 10 value 7577.708999
## iter 20 value 7577.690408
## final value 7577.690237
## converged
## # weights: 75
## initial value 9085.596067
## iter 10 value 7577.567382
## iter 20 value 7577.531134
## iter 20 value 7577.531102
## final value 7577.531002
## converged
## # weights: 121
## initial value 8540.012849
## iter 10 value 7577.691150
## iter 20 value 7577.608801
## iter 30 value 5946.207892
## iter 40 value 5491.104393
## iter 50 value 5409.434680
## iter 60 value 5235.900425
## iter 70 value 4712.605687
## iter 80 value 4221.717486
## iter 90 value 3907.546918
## iter 100 value 3727.824629
## final value 3727.824629
## stopped after 100 iterations
## # weights: 167
## initial value 8394.060527
## iter 10 value 7577.749471
## iter 20 value 7577.234884
## iter 30 value 5818.056771
## iter 40 value 5473.054691
## iter 50 value 5391.601875
## iter 60 value 5314.983351
## iter 70 value 4032.892554
## iter 80 value 3706.201068
## iter 90 value 3601.872870
## iter 100 value 2974.106494
## final value 2974.106494
## stopped after 100 iterations
## # weights: 167
## initial value 15593.541906
## iter 10 value 11692.316597
## iter 20 value 11373.261503
## iter 30 value 11365.905610
## iter 40 value 11322.255996
## iter 50 value 11053.343973
## iter 60 value 9577.943969
## iter 70 value 9261.906148
## iter 80 value 9040.193812
## iter 90 value 8913.218857
## iter 100 value 8328.652598
## final value 8328.652598
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n2_NN1Fit0
## Neural Network
##
## 7002 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4667, 4669, 4668
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.4115967 0.1766426
## 2 0.5 0.3800343 0.1229214
## 2 0.7 0.3827861 0.1281538
## 3 0.3 0.2934876 0.0000000
## 3 0.5 0.5309946 0.3497537
## 3 0.7 0.2934876 0.0000000
## 5 0.3 0.3821766 0.1258834
## 5 0.5 0.5276008 0.3553392
## 5 0.7 0.7420541 0.6591329
## 7 0.3 0.4450807 0.2223132
## 7 0.5 0.3949307 0.1531822
## 7 0.7 0.7469392 0.6701730
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_KDE_5.60.5_n2_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.7539649 0.6754634 Fold2
## 2 0.6856531 0.5941632 Fold1
## 3 0.8011997 0.7408924 Fold3
nb_tda_kde_5.60.5_n2_nn1_fit_re<-DryBean_TDA_KDE_5.60.5_n2_NN1Fit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n2_NN1Fit0)
## a 16-7-6 network with 167 weights
## options were - softmax modelling decay=0.7
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.00 -0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.00 -0.11 0.00 0.00 0.00 0.00 0.00 -0.11 0.00 0.00
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.00 0.00 -0.05 0.04 -0.02 0.00 0.00 0.00 0.00 0.00
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4 i9->h4
## 0.00 -0.06 0.00 0.00 0.00 0.00 0.00 -0.06 0.00 0.00
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5 i9->h5
## 0.00 -0.01 -0.04 0.00 -0.03 0.00 0.00 0.01 -0.02 0.00
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6 i9->h6
## 0.00 -0.01 0.00 0.00 0.00 0.00 0.00 -0.01 0.00 0.00
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7 i9->h7
## 0.00 0.00 0.02 0.36 0.80 -0.01 -0.03 -0.01 -0.58 0.02
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7
## 0.00 0.00 0.01 0.00 0.00 0.02 0.00
## b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1
## 0.59 0.68 -0.10 -0.10 0.56 0.68 -0.10 -4.55
## b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2
## 0.97 0.99 -0.03 -0.09 1.28 0.99 -0.03 -5.28
## b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3
## -0.56 -0.59 0.03 0.07 -0.56 -0.59 0.03 3.38
## b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4
## -0.03 -0.09 0.06 0.00 -0.17 -0.09 0.06 0.92
## b->o5 h1->o5 h2->o5 h3->o5 h4->o5 h5->o5 h6->o5 h7->o5
## -1.01 -1.01 0.00 0.00 -0.89 -1.01 0.00 3.90
## b->o6 h1->o6 h2->o6 h3->o6 h4->o6 h5->o6 h6->o6 h7->o6
## 0.05 0.01 0.04 0.12 -0.21 0.02 0.04 1.62
#vip(DryBean_TDA_KDE_5.60.5_n2_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n2_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n2_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n2_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n2_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n2_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 320 156 468 0 136 0 1
## DERMASON 1 0 3 1057 62 575 507
## HOROZ 0 0 0 0 0 0 0
## SEKER 0 0 0 0 0 0 0
## SIRA 75 0 18 6 380 33 282
##
## Overall Statistics
##
## Accuracy : 0.4429
## 95% CI : (0.4276, 0.4583)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2946
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.9571 0.9944
## Specificity 1.00000 1.00000 0.8293 0.6195
## Pos Pred Value NaN NaN 0.4329 0.4794
## Neg Pred Value 0.90294 0.96176 0.9930 0.9968
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.1147 0.2591
## Detection Prevalence 0.00000 0.00000 0.2650 0.5404
## Balanced Accuracy 0.50000 0.50000 0.8932 0.8069
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.000 0.35696
## Specificity 1.0000 1.000 0.84438
## Pos Pred Value NaN NaN 0.35516
## Neg Pred Value 0.8583 0.851 0.84540
## Prevalence 0.1417 0.149 0.19363
## Detection Rate 0.0000 0.000 0.06912
## Detection Prevalence 0.0000 0.000 0.19461
## Balanced Accuracy 0.5000 0.500 0.60067
nb_tda_kde_5.60.5_n2_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 320 156 468 0 136 0 1
## DERMASON 1 0 3 1057 62 575 507
## HOROZ 0 0 0 0 0 0 0
## SEKER 0 0 0 0 0 0 0
## SIRA 75 0 18 6 380 33 282
##
## Overall Statistics
##
## Accuracy : 0.4429
## 95% CI : (0.4276, 0.4583)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2946
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.9571 0.9944
## Specificity 1.00000 1.00000 0.8293 0.6195
## Pos Pred Value NaN NaN 0.4329 0.4794
## Neg Pred Value 0.90294 0.96176 0.9930 0.9968
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.1147 0.2591
## Detection Prevalence 0.00000 0.00000 0.2650 0.5404
## Balanced Accuracy 0.50000 0.50000 0.8932 0.8069
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.000 0.35696
## Specificity 1.0000 1.000 0.84438
## Pos Pred Value NaN NaN 0.35516
## Neg Pred Value 0.8583 0.851 0.84540
## Prevalence 0.1417 0.149 0.19363
## Detection Rate 0.0000 0.000 0.06912
## Detection Prevalence 0.0000 0.000 0.19461
## Balanced Accuracy 0.5000 0.500 0.60067
nb_tda_kde_5.60.5_n2_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.428922e-01 2.945835e-01 4.275733e-01 4.582929e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 3.159817e-139 NaN
nb_tda_kde_5.60.5_n2_db_nn1_cf0_ov_acc<-nb_tda_kde_5.60.5_n2_db_nn1_cf0$overall[1]
nb_tda_kde_5.60.5_n2_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9570552 0.8292955 0.4329325 0.9929977 0.4329325
## Class: DERMASON 0.9943556 0.6194896 0.4793651 0.9968000 0.4793651
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.3569620 0.8443769 0.3551637 0.8454047 0.3551637
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.00000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.9570552 0.5961783 0.11985294 0.11470588
## Class: DERMASON 0.9943556 0.6468788 0.26053922 0.25906863
## Class: HOROZ 0.0000000 NA 0.14166667 0.00000000
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.3569620 0.3560606 0.19362745 0.06911765
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.2649510 0.8931753
## Class: DERMASON 0.5404412 0.8069226
## Class: HOROZ 0.0000000 0.5000000
## Class: SEKER 0.0000000 0.5000000
## Class: SIRA 0.1946078 0.6006695
nb_tda_kde_5.60.5_n2_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n2_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.60.5_n2_nn1_fit_re)
diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold
## Accuracy
## 1 -0.27961798
## 2 0.05372367
## 3 -0.54089119
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n2_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n2_3_fold_odds.left<-bst_tda_kde_5.60.5_nn1.n2_3_fold$probLeft/bst_tda_kde_5.60.5_nn1.n2_3_fold$probRight
bst_tda_kde_5.60.5_nn1.n2_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n2_3_fold
## $winLeft
## [1] 0.8762667
##
## $winRope
## [1] 0.0156
##
## $winRight
## [1] 0.1081333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n2_3_fold
## $left
## [1] 0.8290468
##
## $rope
## [1] 0.01441263
##
## $right
## [1] 0.1565405
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold))
#bf_tda_kde_5.60.5_nn1.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n2_3_fold)
## t = -1.4854, df = 2, p-value = 0.2757
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9959538 0.4847634
## sample estimates:
## mean of x
## -0.2555952
### Test set diff
diff_drybean_tda_kde_5.60.5_nn1.n2_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.60.5_n2_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nn1.n2_test
## Accuracy
## -0.06495098
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n2_test),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n2_test
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n2_test_odds.left<-bst_tda_kde_5.60.5_nn1.n2_test$probLeft/bst_tda_kde_5.60.5_nn1.n2_test$probRight
bst_tda_kde_5.60.5_nn1.n2_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n2_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n2_test
## $winLeft
## [1] 0.8358333
##
## $winRope
## [1] 0.1641667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nn1.n2_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n2_test)) #bf_tda_pca_5.60.5_nn1.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n2_test))
##Node3
#Neural Network 1
DryBean_TDA_KDE_5.60.5_n3_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n3.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 49
## initial value 4860.083526
## iter 10 value 2662.370285
## iter 20 value 2646.981310
## iter 30 value 2553.393035
## iter 40 value 2171.341259
## iter 50 value 1654.661006
## iter 60 value 1373.079004
## iter 70 value 983.182679
## iter 80 value 828.941189
## iter 90 value 773.759723
## iter 100 value 715.332043
## final value 715.332043
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 71
## initial value 5566.629028
## iter 10 value 2671.774833
## iter 20 value 2333.936176
## iter 30 value 2168.438968
## iter 40 value 1848.834236
## iter 50 value 1812.944296
## iter 60 value 1754.449088
## iter 70 value 1726.571688
## iter 80 value 1701.484497
## iter 90 value 1641.114385
## iter 100 value 1593.530120
## final value 1593.530120
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 115
## initial value 3924.437445
## iter 10 value 2676.601900
## iter 20 value 2270.019036
## iter 30 value 1973.606533
## iter 40 value 1830.709047
## iter 50 value 1767.839509
## iter 60 value 1709.045642
## iter 70 value 1646.970374
## iter 80 value 1588.122440
## iter 90 value 1259.911115
## iter 100 value 836.697822
## final value 836.697822
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 159
## initial value 4418.096181
## iter 10 value 2672.675221
## iter 20 value 2661.727469
## iter 30 value 2600.137957
## iter 40 value 2122.426265
## iter 50 value 1874.699981
## iter 60 value 1812.317273
## iter 70 value 1784.310124
## iter 80 value 1204.275582
## iter 90 value 969.069646
## iter 100 value 899.659809
## final value 899.659809
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 49
## initial value 3459.884174
## iter 10 value 2692.856273
## iter 20 value 2680.254559
## iter 30 value 2630.774143
## iter 40 value 2352.609223
## iter 50 value 2132.769297
## iter 60 value 1997.092092
## iter 70 value 1884.275216
## iter 80 value 1734.788180
## iter 90 value 1644.710903
## iter 100 value 1515.298198
## final value 1515.298198
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 71
## initial value 5143.895521
## iter 10 value 2708.004530
## iter 20 value 2663.969202
## iter 30 value 2662.388520
## iter 40 value 2662.285372
## iter 50 value 2662.240233
## final value 2662.239712
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 115
## initial value 4631.758686
## iter 10 value 2690.652281
## iter 20 value 2667.197207
## iter 30 value 2661.860404
## iter 40 value 2661.729846
## iter 50 value 2661.705121
## iter 60 value 2661.396108
## iter 70 value 2661.340199
## final value 2661.337060
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 159
## initial value 5175.127650
## iter 10 value 2725.649006
## iter 20 value 2704.259997
## iter 30 value 2663.812254
## iter 40 value 2663.749017
## iter 50 value 2661.699197
## iter 60 value 2500.486675
## iter 70 value 2422.211548
## iter 80 value 1949.665658
## iter 90 value 1438.945730
## iter 100 value 1083.057087
## final value 1083.057087
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 49
## initial value 4422.264837
## iter 10 value 2670.641203
## iter 20 value 2666.791603
## iter 30 value 2665.772003
## iter 40 value 2447.051586
## iter 50 value 1994.743620
## iter 60 value 1841.340565
## iter 70 value 1706.759542
## iter 80 value 1591.341005
## iter 90 value 1571.937891
## iter 100 value 1528.316431
## final value 1528.316431
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 71
## initial value 4909.756460
## iter 10 value 2679.556808
## iter 20 value 2666.495097
## iter 30 value 2664.004078
## iter 40 value 2014.222064
## iter 50 value 1397.648698
## iter 60 value 1060.330593
## iter 70 value 916.051544
## iter 80 value 845.791320
## iter 90 value 808.176772
## iter 100 value 770.344968
## final value 770.344968
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 115
## initial value 4228.711205
## iter 10 value 2714.032989
## iter 20 value 2452.709840
## iter 30 value 2341.762144
## iter 40 value 1919.096672
## iter 50 value 1202.783045
## iter 60 value 1092.345047
## iter 70 value 1011.541519
## iter 80 value 940.017673
## iter 90 value 886.632003
## iter 100 value 836.237297
## final value 836.237297
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'CALI' is empty
## # weights: 159
## initial value 5289.003646
## iter 10 value 3024.329195
## iter 20 value 2669.851256
## iter 30 value 2664.735012
## iter 40 value 2664.587400
## iter 50 value 2661.838751
## iter 60 value 2661.701052
## final value 2661.699646
## converged
## # weights: 52
## initial value 5292.149080
## iter 10 value 2672.651214
## iter 20 value 2669.562134
## iter 30 value 2161.298289
## iter 40 value 1874.907754
## iter 50 value 1786.414203
## iter 60 value 1108.786195
## iter 70 value 905.329376
## iter 80 value 787.330660
## iter 90 value 751.542397
## iter 100 value 746.481101
## final value 746.481101
## stopped after 100 iterations
## # weights: 75
## initial value 6052.532786
## iter 10 value 2672.639926
## iter 20 value 2671.975314
## iter 30 value 2671.728129
## iter 40 value 2671.651252
## iter 50 value 2671.639770
## final value 2671.639719
## converged
## # weights: 121
## initial value 5265.516094
## iter 10 value 2788.276939
## iter 20 value 2673.618218
## iter 30 value 2670.835625
## iter 40 value 2670.787301
## iter 50 value 2670.682789
## final value 2670.657038
## converged
## # weights: 167
## initial value 3933.521909
## iter 10 value 2732.508122
## iter 20 value 2673.363722
## iter 30 value 2671.649914
## iter 40 value 2393.790679
## iter 50 value 1931.714843
## iter 60 value 1913.565507
## iter 70 value 1744.782498
## iter 80 value 1590.826412
## iter 90 value 1296.129237
## iter 100 value 1169.970220
## final value 1169.970220
## stopped after 100 iterations
## # weights: 52
## initial value 4581.715798
## iter 10 value 2722.609544
## iter 20 value 2707.721525
## iter 30 value 2119.082892
## iter 40 value 1924.180024
## iter 50 value 1262.142884
## iter 60 value 1098.478525
## iter 70 value 977.627442
## iter 80 value 929.059682
## iter 90 value 899.321114
## iter 100 value 864.563368
## final value 864.563368
## stopped after 100 iterations
## # weights: 75
## initial value 4094.444712
## iter 10 value 2687.830155
## iter 20 value 2678.332794
## iter 30 value 2676.006315
## iter 40 value 2631.181632
## iter 50 value 1657.628662
## iter 60 value 1129.247768
## iter 70 value 1082.282744
## iter 80 value 1046.384148
## iter 90 value 1018.174293
## iter 100 value 996.921238
## final value 996.921238
## stopped after 100 iterations
## # weights: 121
## initial value 4494.716065
## iter 10 value 2684.285951
## iter 20 value 2680.217638
## iter 30 value 2672.897060
## iter 40 value 2671.964758
## final value 2671.963992
## converged
## # weights: 167
## initial value 4312.002814
## iter 10 value 2716.606135
## iter 20 value 2685.844158
## iter 30 value 2674.662964
## iter 40 value 2673.434155
## iter 50 value 2392.663687
## iter 60 value 2286.868430
## iter 70 value 1997.478800
## iter 80 value 1225.008711
## iter 90 value 1183.790968
## iter 100 value 1038.584733
## final value 1038.584733
## stopped after 100 iterations
## # weights: 52
## initial value 4331.227013
## iter 10 value 2783.631538
## iter 20 value 2688.828237
## iter 30 value 2611.731546
## iter 40 value 2080.527621
## iter 50 value 1883.791458
## iter 60 value 1753.480636
## iter 70 value 1646.568457
## iter 80 value 1618.392925
## iter 90 value 1521.518031
## iter 100 value 1185.695669
## final value 1185.695669
## stopped after 100 iterations
## # weights: 75
## initial value 5257.777079
## iter 10 value 2721.333065
## iter 20 value 2682.984796
## iter 30 value 2681.213752
## iter 40 value 2453.987657
## iter 50 value 2081.678587
## iter 60 value 2021.741279
## iter 70 value 1788.215351
## iter 80 value 1376.896443
## iter 90 value 1093.633788
## iter 100 value 991.367262
## final value 991.367262
## stopped after 100 iterations
## # weights: 121
## initial value 4448.999239
## iter 10 value 2953.609400
## iter 20 value 2950.767477
## iter 30 value 2414.200864
## iter 40 value 1962.144502
## iter 50 value 1897.686092
## iter 60 value 1847.130326
## iter 70 value 1815.562811
## iter 80 value 1774.403556
## iter 90 value 1693.073602
## iter 100 value 1660.643773
## final value 1660.643773
## stopped after 100 iterations
## # weights: 167
## initial value 6029.557264
## iter 10 value 2747.793426
## iter 20 value 2728.596526
## iter 30 value 2673.942303
## iter 40 value 2673.417122
## iter 50 value 2673.403389
## iter 60 value 2673.259516
## iter 70 value 2672.517439
## iter 80 value 2662.359377
## iter 90 value 2404.283646
## iter 100 value 2301.457303
## final value 2301.457303
## stopped after 100 iterations
## # weights: 52
## initial value 3681.508776
## iter 10 value 2688.841368
## iter 20 value 2673.800333
## iter 30 value 2358.952942
## iter 40 value 2074.903798
## iter 50 value 1737.553065
## iter 60 value 1066.038900
## iter 70 value 948.115764
## iter 80 value 921.780358
## iter 90 value 896.133093
## iter 100 value 874.841089
## final value 874.841089
## stopped after 100 iterations
## # weights: 75
## initial value 5347.761913
## iter 10 value 2675.500791
## iter 20 value 2675.291060
## iter 30 value 2675.107338
## iter 40 value 2671.290704
## iter 50 value 2278.097746
## iter 60 value 2267.716638
## iter 70 value 1888.922748
## iter 80 value 1202.150195
## iter 90 value 971.299784
## iter 100 value 905.507516
## final value 905.507516
## stopped after 100 iterations
## # weights: 121
## initial value 3494.855233
## iter 10 value 2681.774624
## iter 20 value 2672.651097
## iter 30 value 2672.267984
## iter 40 value 2672.235851
## iter 50 value 2670.428349
## iter 60 value 2589.959413
## iter 70 value 2236.191882
## iter 80 value 1775.284689
## iter 90 value 1702.854049
## iter 100 value 1692.755170
## final value 1692.755170
## stopped after 100 iterations
## # weights: 167
## initial value 3110.752642
## iter 10 value 2691.315406
## iter 20 value 2673.333289
## iter 30 value 2671.806326
## iter 40 value 2632.725439
## iter 50 value 2003.756569
## iter 60 value 1973.311680
## iter 70 value 1880.429061
## iter 80 value 1863.867984
## iter 90 value 1839.308163
## iter 100 value 1456.240013
## final value 1456.240013
## stopped after 100 iterations
## # weights: 52
## initial value 5437.471091
## iter 10 value 2699.297896
## iter 20 value 2678.365233
## iter 30 value 2677.666630
## iter 40 value 2675.105637
## iter 50 value 2673.567011
## iter 60 value 2477.467261
## iter 70 value 2006.836843
## iter 80 value 1922.314039
## iter 90 value 1293.849797
## iter 100 value 1095.751316
## final value 1095.751316
## stopped after 100 iterations
## # weights: 75
## initial value 4684.809639
## iter 10 value 2713.019075
## iter 20 value 2674.875230
## iter 30 value 2674.726053
## iter 40 value 2673.202569
## iter 50 value 2664.552844
## iter 60 value 2643.372687
## iter 70 value 2507.695340
## iter 80 value 1971.259407
## iter 90 value 1773.507394
## iter 100 value 1697.310546
## final value 1697.310546
## stopped after 100 iterations
## # weights: 121
## initial value 4023.917786
## iter 10 value 2720.060374
## iter 20 value 2680.284538
## iter 30 value 2677.552229
## iter 40 value 2675.959324
## iter 50 value 2674.173805
## iter 60 value 2440.456422
## iter 70 value 2024.789575
## iter 80 value 1869.863120
## iter 90 value 1264.892859
## iter 100 value 1021.566591
## final value 1021.566591
## stopped after 100 iterations
## # weights: 167
## initial value 3470.156061
## iter 10 value 2679.755426
## iter 20 value 2550.248956
## iter 30 value 2414.332325
## iter 40 value 1868.276289
## iter 50 value 1737.995592
## iter 60 value 1524.866210
## iter 70 value 1405.930951
## iter 80 value 1139.785097
## iter 90 value 1024.277986
## iter 100 value 916.465824
## final value 916.465824
## stopped after 100 iterations
## # weights: 52
## initial value 3806.255392
## iter 10 value 2703.178859
## iter 20 value 2681.187538
## iter 30 value 2391.905975
## iter 40 value 2012.793390
## iter 50 value 1815.718459
## iter 60 value 1690.857113
## iter 70 value 1592.242371
## iter 80 value 1461.384688
## iter 90 value 1435.275903
## iter 100 value 1434.270742
## final value 1434.270742
## stopped after 100 iterations
## # weights: 75
## initial value 4540.050895
## iter 10 value 2708.105852
## iter 20 value 2679.325157
## iter 30 value 2584.645361
## iter 40 value 2296.921682
## iter 50 value 2014.381714
## iter 60 value 1787.118525
## iter 70 value 1765.321518
## iter 80 value 1724.691396
## iter 90 value 1613.302653
## iter 100 value 1571.908564
## final value 1571.908564
## stopped after 100 iterations
## # weights: 121
## initial value 5344.189921
## iter 10 value 2696.748045
## iter 20 value 2686.942794
## iter 30 value 2681.310235
## iter 40 value 2680.771843
## iter 50 value 2159.839737
## iter 60 value 1941.954879
## iter 70 value 1873.485028
## iter 80 value 1836.093447
## iter 90 value 1797.989539
## iter 100 value 1469.672000
## final value 1469.672000
## stopped after 100 iterations
## # weights: 167
## initial value 6717.805653
## iter 10 value 2689.655475
## iter 20 value 2673.684833
## iter 30 value 2672.570845
## iter 40 value 2672.028067
## iter 50 value 2671.469501
## iter 60 value 2657.388095
## iter 70 value 2632.522790
## iter 80 value 2610.250044
## iter 90 value 2592.187673
## iter 100 value 2377.908967
## final value 2377.908967
## stopped after 100 iterations
## # weights: 75
## initial value 6287.549374
## iter 10 value 4129.454831
## iter 20 value 4010.462816
## iter 30 value 3670.161727
## iter 40 value 2943.186051
## iter 50 value 2900.473625
## iter 60 value 2729.296774
## iter 70 value 2664.501729
## iter 80 value 2580.886373
## iter 90 value 2179.292609
## iter 100 value 1583.841830
## final value 1583.841830
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n3_NN1Fit0
## Neural Network
##
## 3511 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 2340, 2341, 2341
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.6028468 0.5300243
## 2 0.5 0.6407050 0.5656450
## 2 0.7 0.5675141 0.4859431
## 3 0.3 0.4233591 0.2292670
## 3 0.5 0.6685902 0.5440594
## 3 0.7 0.5213651 0.4028616
## 5 0.3 0.3518506 0.1151301
## 5 0.5 0.5087611 0.2760156
## 5 0.7 0.5393145 0.4348135
## 7 0.3 0.5623866 0.4629263
## 7 0.5 0.5977157 0.5186298
## 7 0.7 0.4460830 0.1830201
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 3 and decay = 0.5.
DryBean_TDA_KDE_5.60.5_n3_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.8623932 0.7917841 Fold3
## 2 0.8948718 0.8403942 Fold2
## 3 0.2485056 0.0000000 Fold1
nb_tda_kde_5.60.5_n3_nn1_fit_re<-DryBean_TDA_KDE_5.60.5_n3_NN1Fit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n3_NN1Fit0)
## a 16-3-6 network with 75 weights
## options were - softmax modelling decay=0.5
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## -0.01 0.00 -0.02 0.48 0.51 0.42 0.12 0.00 -0.90 0.18
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## -0.01 -0.12 -0.15 0.00 0.00 -0.24 -0.02
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.06 -0.01 -0.06 -0.64 -0.80 0.11 -0.02 0.01 1.68 -0.11
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.06 0.11 0.08 0.00 0.00 0.11 0.10
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## -0.02 0.00 0.21 0.76 1.87 -0.04 -0.18 0.01 -4.12 0.10
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## -0.02 -0.04 0.02 0.00 0.00 0.06 -0.04
## b->o1 h1->o1 h2->o1 h3->o1
## -3.37 -3.41 3.22 0.61
## b->o2 h1->o2 h2->o2 h3->o2
## -1.26 -0.68 0.61 -0.02
## b->o3 h1->o3 h2->o3 h3->o3
## 1.45 3.63 -1.78 -4.75
## b->o4 h1->o4 h2->o4 h3->o4
## -1.35 1.64 -0.53 0.24
## b->o5 h1->o5 h2->o5 h3->o5
## 4.66 -4.67 0.38 2.12
## b->o6 h1->o6 h2->o6 h3->o6
## -0.13 3.49 -1.89 1.81
#vip(DryBean_TDA_KDE_5.60.5_n3_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n3_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n3_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n3_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n3_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n3_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 0 0 0 1011 24 20 111
## HOROZ 0 0 0 0 0 0 0
## SEKER 71 136 7 6 0 558 9
## SIRA 325 20 482 46 554 30 670
##
## Overall Statistics
##
## Accuracy : 0.5488
## 95% CI : (0.5334, 0.5641)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.433
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9511
## Specificity 1.00000 1.00000 1.0000 0.9486
## Pos Pred Value NaN NaN NaN 0.8671
## Neg Pred Value 0.90294 0.96176 0.8801 0.9822
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2478
## Detection Prevalence 0.00000 0.00000 0.0000 0.2858
## Balanced Accuracy 0.50000 0.50000 0.5000 0.9499
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9178 0.8481
## Specificity 1.0000 0.9340 0.5571
## Pos Pred Value NaN 0.7090 0.3150
## Neg Pred Value 0.8583 0.9848 0.9386
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1368 0.1642
## Detection Prevalence 0.0000 0.1929 0.5213
## Balanced Accuracy 0.5000 0.9259 0.7026
nb_tda_kde_5.60.5_n3_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 0 0 0 1011 24 20 111
## HOROZ 0 0 0 0 0 0 0
## SEKER 71 136 7 6 0 558 9
## SIRA 325 20 482 46 554 30 670
##
## Overall Statistics
##
## Accuracy : 0.5488
## 95% CI : (0.5334, 0.5641)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.433
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9511
## Specificity 1.00000 1.00000 1.0000 0.9486
## Pos Pred Value NaN NaN NaN 0.8671
## Neg Pred Value 0.90294 0.96176 0.8801 0.9822
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2478
## Detection Prevalence 0.00000 0.00000 0.0000 0.2858
## Balanced Accuracy 0.50000 0.50000 0.5000 0.9499
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9178 0.8481
## Specificity 1.0000 0.9340 0.5571
## Pos Pred Value NaN 0.7090 0.3150
## Neg Pred Value 0.8583 0.9848 0.9386
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1368 0.1642
## Detection Prevalence 0.0000 0.1929 0.5213
## Balanced Accuracy 0.5000 0.9259 0.7026
nb_tda_kde_5.60.5_n3_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5487745 0.4330303 0.5333526 0.5641266 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n3_db_nn1_cf0_ov_acc<-nb_tda_kde_5.60.5_n3_db_nn1_cf0$overall[1]
nb_tda_kde_5.60.5_n3_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9510818 0.9486245 0.8670669 0.9821551 0.8670669
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.9177632 0.9340438 0.7090216 0.9848163 0.7090216
## Class: SIRA 0.8481013 0.5571429 0.3149976 0.9385561 0.3149976
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.0000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.0000000
## Class: CALI 0.0000000 NA 0.11985294 0.0000000
## Class: DERMASON 0.9510818 0.9071332 0.26053922 0.2477941
## Class: HOROZ 0.0000000 NA 0.14166667 0.0000000
## Class: SEKER 0.9177632 0.8000000 0.14901961 0.1367647
## Class: SIRA 0.8481013 0.4593761 0.19362745 0.1642157
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.0000000 0.5000000
## Class: DERMASON 0.2857843 0.9498532
## Class: HOROZ 0.0000000 0.5000000
## Class: SEKER 0.1928922 0.9259035
## Class: SIRA 0.5213235 0.7026221
nb_tda_kde_5.60.5_n3_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n3_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.60.5_n3_nn1_fit_re)
diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold
## Accuracy
## 1 -0.38804629
## 2 -0.15549502
## 3 0.01180292
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n3_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n3_3_fold_odds.left<-bst_tda_kde_5.60.5_nn1.n3_3_fold$probLeft/bst_tda_kde_5.60.5_nn1.n3_3_fold$probRight
bst_tda_kde_5.60.5_nn1.n3_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n3_3_fold
## $winLeft
## [1] 0.8839
##
## $winRope
## [1] 0.05266667
##
## $winRight
## [1] 0.06343333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n3_3_fold
## $left
## [1] 0.8310269
##
## $rope
## [1] 0.02056886
##
## $right
## [1] 0.1484043
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold))
#bf_tda_kde_5.60.5_nn1.n3_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n3_3_fold)
## t = -1.5288, df = 2, p-value = 0.2659
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6760860 0.3215937
## sample estimates:
## mean of x
## -0.1772461
### Test set diff
diff_drybean_tda_kde_5.60.5_nn1.n3_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.60.5_n3_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nn1.n3_test
## Accuracy
## -0.1708333
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n3_test),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n3_test
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n3_test_odds.left<-bst_tda_kde_5.60.5_nn1.n3_test$probLeft/bst_tda_kde_5.60.5_nn1.n3_test$probRight
bst_tda_kde_5.60.5_nn1.n3_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n3_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n3_test
## $winLeft
## [1] 0.8394667
##
## $winRope
## [1] 0.1605333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nn1.n3_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n3_test)) #bf_tda_pca_5.60.5_nn1.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n3_test))
##Node4
#Neural Network 1
DryBean_TDA_KDE_5.60.5_n4_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n4.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 46
## initial value 1481.368366
## iter 10 value 1209.375420
## iter 20 value 1208.237419
## iter 30 value 1195.537485
## iter 40 value 1072.247248
## iter 50 value 1064.886303
## iter 60 value 1056.225639
## iter 70 value 837.945103
## iter 80 value 669.256476
## iter 90 value 662.291500
## iter 100 value 649.783542
## final value 649.783542
## stopped after 100 iterations
## # weights: 67
## initial value 2378.693702
## iter 10 value 1224.582047
## iter 20 value 1209.145849
## iter 30 value 1208.304698
## iter 40 value 1208.007923
## iter 50 value 1207.594594
## iter 60 value 1207.205077
## iter 70 value 1207.201249
## final value 1207.201230
## converged
## # weights: 109
## initial value 1519.900897
## iter 10 value 1208.623236
## iter 20 value 1208.254807
## iter 30 value 1079.347076
## iter 40 value 785.405701
## iter 50 value 682.103962
## iter 60 value 662.893865
## iter 70 value 652.748038
## iter 80 value 641.460269
## iter 90 value 640.338921
## iter 100 value 640.221158
## final value 640.221158
## stopped after 100 iterations
## # weights: 151
## initial value 2038.407245
## iter 10 value 1213.642837
## iter 20 value 1206.885170
## iter 30 value 993.515613
## iter 40 value 812.219885
## iter 50 value 719.734796
## iter 60 value 690.985485
## iter 70 value 650.662775
## iter 80 value 604.807993
## iter 90 value 566.227069
## iter 100 value 549.643156
## final value 549.643156
## stopped after 100 iterations
## # weights: 46
## initial value 1381.153590
## iter 10 value 1215.951777
## iter 20 value 1208.774466
## iter 30 value 1208.530465
## final value 1208.526671
## converged
## # weights: 67
## initial value 1679.748983
## iter 10 value 1211.586116
## iter 20 value 1209.236355
## iter 30 value 1208.532875
## iter 40 value 1208.416480
## iter 50 value 1208.141165
## iter 60 value 1205.517213
## iter 70 value 1197.434823
## iter 80 value 1083.156342
## iter 90 value 845.343774
## iter 100 value 731.316718
## final value 731.316718
## stopped after 100 iterations
## # weights: 109
## initial value 1751.868987
## iter 10 value 1223.108155
## iter 20 value 1211.250690
## iter 30 value 1209.668682
## iter 40 value 1166.251999
## iter 50 value 860.836081
## iter 60 value 760.266675
## iter 70 value 730.381879
## iter 80 value 708.470441
## iter 90 value 679.705477
## iter 100 value 657.899224
## final value 657.899224
## stopped after 100 iterations
## # weights: 151
## initial value 1945.176703
## iter 10 value 1210.599742
## iter 20 value 1207.597244
## iter 30 value 1207.329538
## iter 40 value 1207.279425
## iter 50 value 1107.574043
## iter 60 value 1080.655296
## iter 70 value 1023.165168
## iter 80 value 715.341726
## iter 90 value 638.085419
## iter 100 value 582.152385
## final value 582.152385
## stopped after 100 iterations
## # weights: 46
## initial value 1887.741462
## iter 10 value 1243.736045
## iter 20 value 1237.909437
## iter 30 value 1171.769626
## iter 40 value 852.184319
## iter 50 value 727.771114
## iter 60 value 707.777158
## iter 70 value 675.212027
## iter 80 value 665.477204
## iter 90 value 664.719079
## iter 100 value 664.548578
## final value 664.548578
## stopped after 100 iterations
## # weights: 67
## initial value 1441.600930
## iter 10 value 1210.221130
## iter 20 value 1209.462802
## iter 30 value 1209.447767
## iter 30 value 1209.447758
## iter 40 value 1208.663458
## final value 1208.644256
## converged
## # weights: 109
## initial value 2424.444203
## iter 10 value 1219.081283
## iter 20 value 1177.449358
## iter 30 value 1078.071644
## iter 40 value 1048.408084
## iter 50 value 934.608793
## iter 60 value 854.575887
## iter 70 value 721.560730
## iter 80 value 688.432632
## iter 90 value 671.125231
## iter 100 value 665.338223
## final value 665.338223
## stopped after 100 iterations
## # weights: 151
## initial value 2515.029150
## iter 10 value 1228.355558
## iter 20 value 1214.370088
## iter 30 value 1193.893772
## iter 40 value 1184.330659
## iter 50 value 807.702867
## iter 60 value 627.897221
## iter 70 value 595.081331
## iter 80 value 557.929926
## iter 90 value 547.331051
## iter 100 value 541.083673
## final value 541.083673
## stopped after 100 iterations
## # weights: 46
## initial value 2016.481980
## iter 10 value 1206.950801
## iter 20 value 1206.247077
## final value 1206.246965
## converged
## # weights: 67
## initial value 1575.402751
## iter 10 value 1210.987577
## iter 20 value 1207.089862
## iter 30 value 1206.963154
## iter 40 value 1206.113706
## iter 50 value 1205.908051
## iter 60 value 1026.238513
## iter 70 value 831.690111
## iter 80 value 741.298683
## iter 90 value 733.136381
## iter 100 value 684.938859
## final value 684.938859
## stopped after 100 iterations
## # weights: 109
## initial value 1760.690789
## iter 10 value 1236.997493
## iter 20 value 1236.979357
## iter 20 value 1236.979350
## iter 30 value 1209.133758
## iter 40 value 1205.960572
## iter 50 value 1205.760557
## iter 60 value 1205.445671
## iter 70 value 1165.052523
## iter 80 value 1033.912162
## iter 90 value 844.539949
## iter 100 value 765.790718
## final value 765.790718
## stopped after 100 iterations
## # weights: 151
## initial value 2034.510763
## iter 10 value 1208.273859
## iter 20 value 1206.008749
## iter 30 value 1196.417735
## iter 40 value 822.693553
## iter 50 value 743.360827
## iter 60 value 703.089303
## iter 70 value 685.431434
## iter 80 value 681.989757
## iter 90 value 670.218535
## iter 100 value 621.636589
## final value 621.636589
## stopped after 100 iterations
## # weights: 46
## initial value 1869.062957
## iter 10 value 1210.256736
## iter 20 value 1207.258736
## iter 30 value 1207.203626
## final value 1207.203410
## converged
## # weights: 67
## initial value 1936.568802
## iter 10 value 1210.251509
## iter 20 value 1207.286811
## iter 30 value 1207.188957
## iter 40 value 983.199836
## iter 50 value 858.408611
## iter 60 value 833.468990
## iter 70 value 767.863821
## iter 80 value 724.411446
## iter 90 value 700.215003
## iter 100 value 669.702005
## final value 669.702005
## stopped after 100 iterations
## # weights: 109
## initial value 1544.790044
## iter 10 value 1220.508013
## iter 20 value 1208.866641
## iter 30 value 1208.359925
## final value 1208.351250
## converged
## # weights: 151
## initial value 2155.406904
## iter 10 value 1210.856326
## iter 20 value 1207.788439
## iter 30 value 1206.493582
## iter 40 value 1206.412979
## iter 50 value 1060.373528
## iter 60 value 1047.577558
## iter 70 value 903.415823
## iter 80 value 749.608038
## iter 90 value 733.321993
## iter 100 value 703.371786
## final value 703.371786
## stopped after 100 iterations
## # weights: 46
## initial value 2058.922940
## iter 10 value 1210.061637
## iter 20 value 1208.570845
## iter 30 value 1208.126104
## final value 1208.125852
## converged
## # weights: 67
## initial value 1459.299395
## iter 10 value 1208.940342
## iter 20 value 1035.950709
## iter 30 value 824.002335
## iter 40 value 771.330078
## iter 50 value 763.846861
## iter 60 value 724.716992
## iter 70 value 678.506797
## iter 80 value 675.203629
## final value 675.197569
## converged
## # weights: 109
## initial value 1709.890868
## iter 10 value 1217.872656
## iter 20 value 1214.053366
## iter 30 value 1208.825929
## iter 40 value 1208.073991
## iter 50 value 1206.578243
## iter 60 value 1128.023553
## iter 70 value 1051.383082
## iter 80 value 1018.661980
## iter 90 value 999.588183
## iter 100 value 841.328496
## final value 841.328496
## stopped after 100 iterations
## # weights: 151
## initial value 1589.655968
## iter 10 value 1212.897757
## iter 20 value 1208.322406
## iter 30 value 1202.858186
## iter 40 value 1102.881106
## iter 50 value 881.266449
## iter 60 value 777.425164
## iter 70 value 751.608516
## iter 80 value 699.626918
## iter 90 value 667.482239
## iter 100 value 626.304747
## final value 626.304747
## stopped after 100 iterations
## # weights: 46
## initial value 1461.162396
## iter 10 value 1208.565596
## iter 20 value 1208.292260
## iter 30 value 1208.171268
## iter 40 value 1094.531374
## iter 50 value 1055.963514
## iter 60 value 1035.046176
## iter 70 value 930.471429
## iter 80 value 718.621850
## iter 90 value 690.925457
## iter 100 value 681.731734
## final value 681.731734
## stopped after 100 iterations
## # weights: 67
## initial value 1314.767323
## iter 10 value 1211.025800
## iter 20 value 1208.363756
## iter 30 value 1208.290894
## iter 40 value 1207.588257
## iter 50 value 1207.518015
## iter 60 value 1207.243697
## iter 70 value 1207.215084
## iter 80 value 1207.203281
## final value 1207.202671
## converged
## # weights: 109
## initial value 1536.975206
## iter 10 value 1208.809801
## iter 20 value 1207.220495
## iter 30 value 1206.830329
## final value 1206.826711
## converged
## # weights: 151
## initial value 2151.092876
## iter 10 value 1207.531084
## iter 20 value 1206.884980
## iter 30 value 1206.754029
## iter 40 value 1055.360644
## iter 50 value 930.186318
## iter 60 value 821.567957
## iter 70 value 706.870520
## iter 80 value 631.250784
## iter 90 value 629.670129
## iter 100 value 614.613056
## final value 614.613056
## stopped after 100 iterations
## # weights: 46
## initial value 1443.798334
## iter 10 value 1210.735437
## iter 20 value 1209.687741
## iter 30 value 1209.675110
## iter 40 value 1122.408289
## iter 50 value 756.656179
## iter 60 value 694.578488
## iter 70 value 679.459045
## iter 80 value 664.468939
## iter 90 value 664.254557
## final value 664.254401
## converged
## # weights: 67
## initial value 1940.221845
## iter 10 value 1210.520354
## iter 20 value 1208.547137
## iter 30 value 1208.526715
## iter 40 value 1207.917885
## iter 50 value 1187.431581
## iter 60 value 1046.418127
## iter 70 value 1021.743401
## iter 80 value 904.580392
## iter 90 value 775.565699
## iter 100 value 697.213070
## final value 697.213070
## stopped after 100 iterations
## # weights: 109
## initial value 2199.062502
## iter 10 value 1224.422827
## iter 20 value 1210.778512
## iter 30 value 1209.252858
## iter 40 value 1207.330867
## iter 50 value 1172.831998
## iter 60 value 1076.810739
## iter 70 value 809.867780
## iter 80 value 682.998634
## iter 90 value 642.237385
## iter 100 value 610.330356
## final value 610.330356
## stopped after 100 iterations
## # weights: 151
## initial value 2753.334631
## iter 10 value 1244.652576
## iter 20 value 1209.692524
## iter 30 value 1207.087680
## iter 40 value 1207.015324
## final value 1207.015148
## converged
## # weights: 46
## initial value 1809.637317
## iter 10 value 1226.762382
## iter 20 value 1221.809275
## iter 30 value 1215.466266
## iter 40 value 943.217459
## iter 50 value 740.091878
## iter 60 value 697.433318
## iter 70 value 673.212259
## iter 80 value 655.418627
## iter 90 value 636.009744
## iter 100 value 603.606825
## final value 603.606825
## stopped after 100 iterations
## # weights: 67
## initial value 2037.992053
## iter 10 value 1225.525130
## iter 20 value 1211.617976
## iter 30 value 1209.637629
## iter 40 value 1204.357500
## iter 50 value 945.823938
## iter 60 value 770.163777
## iter 70 value 686.641294
## iter 80 value 675.096897
## iter 90 value 673.709825
## iter 100 value 673.219481
## final value 673.219481
## stopped after 100 iterations
## # weights: 109
## initial value 1954.676031
## iter 10 value 1216.886855
## iter 20 value 1210.204346
## iter 30 value 1197.170353
## iter 40 value 1052.897884
## iter 50 value 1031.270218
## iter 60 value 871.545119
## iter 70 value 813.058131
## iter 80 value 766.243450
## iter 90 value 674.367573
## iter 100 value 608.784466
## final value 608.784466
## stopped after 100 iterations
## # weights: 151
## initial value 2214.511346
## iter 10 value 1240.138925
## iter 20 value 1210.986358
## iter 30 value 1210.860875
## iter 40 value 1210.608132
## iter 50 value 1208.812365
## iter 60 value 1208.077988
## iter 70 value 1207.065346
## iter 80 value 1189.622008
## iter 90 value 789.688072
## iter 100 value 703.905415
## final value 703.905415
## stopped after 100 iterations
## # weights: 151
## initial value 3029.702119
## iter 10 value 1832.421216
## iter 20 value 1819.124667
## iter 30 value 1815.414467
## iter 40 value 1276.957132
## iter 50 value 1069.215378
## iter 60 value 1049.135952
## iter 70 value 1035.268886
## iter 80 value 1014.852261
## iter 90 value 1008.481524
## iter 100 value 968.804373
## final value 968.804373
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n4_NN1Fit0
## Neural Network
##
## 1759 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 1173, 1172, 1173
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.6874110 0.3774863
## 2 0.5 0.6123256 0.1928286
## 2 0.7 0.6868421 0.3761985
## 3 0.3 0.6133247 0.1948714
## 3 0.5 0.7640632 0.5610887
## 3 0.7 0.6923938 0.3856020
## 5 0.3 0.6867104 0.3771300
## 5 0.5 0.6908239 0.3850612
## 5 0.7 0.7663424 0.5832126
## 7 0.3 0.7771550 0.6196315
## 7 0.5 0.7066135 0.4221853
## 7 0.7 0.7828326 0.6136298
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.7.
DryBean_TDA_KDE_5.60.5_n4_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.7802385 0.6026666 Fold2
## 2 0.8037543 0.6774363 Fold1
## 3 0.7645051 0.5607864 Fold3
nb_tda_kde_5.60.5_n4_nn1_fit_re<-DryBean_TDA_KDE_5.60.5_n4_NN1Fit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n4_NN1Fit0)
## a 16-7-4 network with 151 weights
## options were - softmax modelling decay=0.7
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.00 0.29 0.01 0.00 0.00 0.00 0.00 0.30 0.00 0.00
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4 i9->h4
## 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5 i9->h5
## -0.02 -0.01 -0.06 1.04 1.05 -0.02 0.00 0.01 -1.86 -0.13
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5
## -0.02 -0.03 -0.03 0.00 0.00 -0.04 -0.05
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6 i9->h6
## -0.01 -0.03 0.40 -0.76 -0.18 -0.01 -0.01 0.03 -0.48 0.01
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6
## -0.01 -0.01 0.00 0.00 0.00 0.00 -0.01
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7 i9->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1
## 0.04 0.04 0.04 0.04 0.04 2.95 -0.37 0.04
## b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2
## -0.39 -0.39 -0.39 -0.39 -0.39 0.53 -0.33 -0.39
## b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3
## 0.55 0.55 0.55 0.55 0.55 -6.72 0.74 0.55
## b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4
## -0.20 -0.20 -0.20 -0.20 -0.20 3.23 -0.04 -0.20
#vip(DryBean_TDA_KDE_5.60.5_n4_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n4_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n4_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n4_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n4_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n4_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 319 44 483 1042 578 34 772
## HOROZ 0 0 0 0 0 0 0
## SEKER 77 112 6 21 0 574 18
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3961
## 95% CI : (0.381, 0.4113)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.207
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9802
## Specificity 1.00000 1.00000 1.0000 0.2609
## Pos Pred Value NaN NaN NaN 0.3185
## Neg Pred Value 0.90294 0.96176 0.8801 0.9740
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2554
## Detection Prevalence 0.00000 0.00000 0.0000 0.8020
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6205
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9441 0.0000
## Specificity 1.0000 0.9326 1.0000
## Pos Pred Value NaN 0.7104 NaN
## Neg Pred Value 0.8583 0.9896 0.8064
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1407 0.0000
## Detection Prevalence 0.0000 0.1980 0.0000
## Balanced Accuracy 0.5000 0.9383 0.5000
nb_tda_kde_5.60.5_n4_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 319 44 483 1042 578 34 772
## HOROZ 0 0 0 0 0 0 0
## SEKER 77 112 6 21 0 574 18
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3961
## 95% CI : (0.381, 0.4113)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.207
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9802
## Specificity 1.00000 1.00000 1.0000 0.2609
## Pos Pred Value NaN NaN NaN 0.3185
## Neg Pred Value 0.90294 0.96176 0.8801 0.9740
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2554
## Detection Prevalence 0.00000 0.00000 0.0000 0.8020
## Balanced Accuracy 0.50000 0.50000 0.5000 0.6205
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9441 0.0000
## Specificity 1.0000 0.9326 1.0000
## Pos Pred Value NaN 0.7104 NaN
## Neg Pred Value 0.8583 0.9896 0.8064
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1407 0.0000
## Detection Prevalence 0.0000 0.1980 0.0000
## Balanced Accuracy 0.5000 0.9383 0.5000
nb_tda_kde_5.60.5_n4_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.960784e-01 2.069796e-01 3.810276e-01 4.112782e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 1.403756e-79 NaN
nb_tda_kde_5.60.5_n4_db_nn1_cf0_ov_acc<-nb_tda_kde_5.60.5_n4_db_nn1_cf0$overall[1]
nb_tda_kde_5.60.5_n4_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9802446 0.2608552 0.3184597 0.9740099 0.3184597
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.9440789 0.9326037 0.7103960 0.9896088 0.7103960
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.0000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.0000000
## Class: CALI 0.0000000 NA 0.11985294 0.0000000
## Class: DERMASON 0.9802446 0.4807382 0.26053922 0.2553922
## Class: HOROZ 0.0000000 NA 0.14166667 0.0000000
## Class: SEKER 0.9440789 0.8107345 0.14901961 0.1406863
## Class: SIRA 0.0000000 NA 0.19362745 0.0000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.0000000 0.5000000
## Class: DERMASON 0.8019608 0.6205499
## Class: HOROZ 0.0000000 0.5000000
## Class: SEKER 0.1980392 0.9383413
## Class: SIRA 0.0000000 0.5000000
nb_tda_kde_5.60.5_n4_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n4_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.60.5_n4_nn1_fit_re)
diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold
## Accuracy
## 1 -0.3058916
## 2 -0.0643775
## 3 -0.5041967
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n4_3_fold_odds.left<-bst_tda_kde_5.60.5_nn1.n4_3_fold$probLeft/bst_tda_kde_5.60.5_nn1.n4_3_fold$probRight
bst_tda_kde_5.60.5_nn1.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n4_3_fold
## $winLeft
## [1] 0.9917667
##
## $winRope
## [1] 0.008233333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n4_3_fold
## $left
## [1] 0.9023549
##
## $rope
## [1] 0.009415571
##
## $right
## [1] 0.08822951
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold))
#bf_tda_kde_5.60.5_nn1.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n4_3_fold)
## t = -2.2921, df = 2, p-value = 0.149
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8386523 0.2556751
## sample estimates:
## mean of x
## -0.2914886
### Test set diff
diff_drybean_tda_kde_5.60.5_nn1.n4_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.60.5_n4_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nn1.n4_test
## Accuracy
## -0.01813725
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n4_test),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n4_test
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n4_test_odds.left<-bst_tda_kde_5.60.5_nn1.n4_test$probLeft/bst_tda_kde_5.60.5_nn1.n4_test$probRight
bst_tda_kde_5.60.5_nn1.n4_test_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n4_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n4_test
## $winLeft
## [1] 0.5405333
##
## $winRope
## [1] 0.4594667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nn1.n4_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n4_test)) #bf_tda_pca_5.60.5_nn1.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n4_test))
##Node5
#Neural Network 1
DryBean_TDA_KDE_5.60.5_n5_NN1Fit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n5.vec,
Importance = T,
method = 'nnet',
trControl = fitControl,
tuneGrid = nn1Grid,
metric='Accuracy')
## # weights: 46
## initial value 920.050666
## iter 10 value 518.522091
## iter 20 value 518.181033
## iter 30 value 516.421330
## iter 40 value 500.595578
## iter 50 value 474.432415
## iter 60 value 470.314197
## iter 70 value 434.859474
## iter 80 value 361.523510
## iter 90 value 347.385125
## iter 100 value 331.220949
## final value 331.220949
## stopped after 100 iterations
## # weights: 67
## initial value 710.807796
## iter 10 value 517.074866
## iter 20 value 516.540055
## iter 30 value 516.313721
## final value 516.305750
## converged
## # weights: 109
## initial value 608.777938
## iter 10 value 516.501836
## iter 20 value 516.102579
## iter 30 value 516.062605
## iter 40 value 506.607225
## iter 50 value 415.667486
## iter 60 value 385.825745
## iter 70 value 357.169086
## iter 80 value 352.901336
## iter 90 value 335.803684
## iter 100 value 325.255891
## final value 325.255891
## stopped after 100 iterations
## # weights: 151
## initial value 751.236047
## iter 10 value 522.235375
## iter 20 value 516.754405
## iter 30 value 516.174422
## iter 40 value 516.149161
## iter 50 value 484.161026
## iter 60 value 385.297357
## iter 70 value 341.455014
## iter 80 value 332.533680
## iter 90 value 328.686299
## iter 100 value 319.942398
## final value 319.942398
## stopped after 100 iterations
## # weights: 46
## initial value 919.513179
## iter 10 value 519.165414
## iter 20 value 518.816057
## iter 30 value 517.790874
## iter 40 value 517.724850
## final value 517.724707
## converged
## # weights: 67
## initial value 691.273745
## iter 10 value 520.221267
## iter 20 value 517.965114
## iter 30 value 517.362140
## iter 40 value 496.388540
## iter 50 value 361.358976
## iter 60 value 324.616398
## iter 70 value 319.335045
## iter 80 value 314.925221
## iter 90 value 314.670600
## final value 314.670503
## converged
## # weights: 109
## initial value 734.734501
## iter 10 value 523.461080
## iter 20 value 517.281541
## iter 30 value 516.487240
## iter 40 value 515.154774
## iter 50 value 504.533290
## iter 60 value 398.854836
## iter 70 value 386.920960
## iter 80 value 339.672375
## iter 90 value 328.972834
## iter 100 value 325.326220
## final value 325.326220
## stopped after 100 iterations
## # weights: 151
## initial value 755.221033
## iter 10 value 526.510247
## iter 20 value 517.653619
## iter 30 value 517.114870
## iter 40 value 516.750950
## iter 50 value 515.996181
## iter 60 value 510.860188
## iter 70 value 403.303173
## iter 80 value 364.546333
## iter 90 value 346.805371
## iter 100 value 321.205189
## final value 321.205189
## stopped after 100 iterations
## # weights: 46
## initial value 738.947612
## iter 10 value 521.603621
## iter 20 value 520.570550
## iter 30 value 517.895143
## iter 40 value 495.492163
## iter 50 value 367.336869
## iter 60 value 359.313410
## iter 70 value 351.738818
## iter 80 value 339.575115
## iter 90 value 326.085423
## iter 100 value 322.438794
## final value 322.438794
## stopped after 100 iterations
## # weights: 67
## initial value 1011.336161
## iter 10 value 528.099930
## iter 20 value 523.501835
## iter 30 value 517.935482
## iter 40 value 517.874167
## iter 50 value 517.858450
## iter 60 value 517.300248
## iter 70 value 512.142429
## iter 80 value 501.802660
## iter 90 value 371.369194
## iter 100 value 347.398235
## final value 347.398235
## stopped after 100 iterations
## # weights: 109
## initial value 605.338978
## iter 10 value 518.752856
## iter 20 value 517.369664
## iter 30 value 517.153675
## iter 40 value 493.924463
## iter 50 value 488.200850
## iter 60 value 448.618675
## iter 70 value 387.774096
## iter 80 value 340.246226
## iter 90 value 339.236636
## iter 100 value 334.842410
## final value 334.842410
## stopped after 100 iterations
## # weights: 151
## initial value 640.989277
## iter 10 value 523.645792
## iter 20 value 517.304208
## iter 30 value 516.978371
## iter 40 value 516.860018
## iter 50 value 516.663498
## iter 60 value 516.611662
## iter 70 value 516.488362
## iter 80 value 441.845570
## iter 90 value 413.687944
## iter 100 value 380.549113
## final value 380.549113
## stopped after 100 iterations
## # weights: 46
## initial value 720.709277
## iter 10 value 517.168109
## iter 20 value 515.549838
## iter 30 value 515.108027
## final value 515.093664
## converged
## # weights: 67
## initial value 622.857376
## iter 10 value 518.024055
## iter 20 value 515.292763
## iter 30 value 514.754444
## iter 40 value 514.687517
## final value 514.687312
## converged
## # weights: 109
## initial value 1030.924103
## iter 10 value 525.325535
## iter 20 value 515.096788
## iter 30 value 458.493598
## iter 40 value 379.096245
## iter 50 value 359.053891
## iter 60 value 355.019334
## iter 70 value 342.102130
## iter 80 value 341.819360
## iter 90 value 341.812049
## iter 90 value 341.812047
## final value 341.812047
## converged
## # weights: 151
## initial value 749.538779
## iter 10 value 514.340459
## iter 20 value 514.251981
## iter 30 value 514.214021
## iter 40 value 514.075718
## iter 50 value 514.033541
## iter 60 value 510.526437
## iter 70 value 451.938435
## iter 80 value 365.160051
## iter 90 value 342.764556
## iter 100 value 341.368149
## final value 341.368149
## stopped after 100 iterations
## # weights: 46
## initial value 786.307620
## iter 10 value 518.521187
## iter 20 value 518.146427
## iter 30 value 507.521915
## iter 40 value 504.441685
## iter 50 value 479.481853
## iter 60 value 423.050476
## iter 70 value 365.987416
## iter 80 value 353.669148
## iter 90 value 351.783125
## final value 351.780265
## converged
## # weights: 67
## initial value 584.199120
## iter 10 value 516.383976
## iter 20 value 515.503590
## iter 30 value 515.484110
## iter 30 value 515.484110
## iter 30 value 515.484110
## final value 515.484110
## converged
## # weights: 109
## initial value 912.089462
## iter 10 value 527.945203
## iter 20 value 516.672662
## iter 30 value 516.114628
## iter 40 value 515.394918
## iter 50 value 510.491974
## iter 60 value 409.917784
## iter 70 value 396.241617
## iter 80 value 393.129422
## iter 90 value 364.234465
## iter 100 value 356.210718
## final value 356.210718
## stopped after 100 iterations
## # weights: 151
## initial value 866.185644
## iter 10 value 519.724327
## iter 20 value 514.763751
## iter 30 value 514.643896
## iter 40 value 514.427894
## iter 50 value 506.912180
## iter 60 value 504.007080
## iter 70 value 454.575500
## iter 80 value 377.741082
## iter 90 value 362.418756
## iter 100 value 353.066814
## final value 353.066814
## stopped after 100 iterations
## # weights: 46
## initial value 773.701428
## iter 10 value 519.917831
## iter 20 value 518.989576
## iter 30 value 498.051814
## iter 40 value 482.933110
## iter 50 value 481.859145
## iter 60 value 480.846090
## iter 70 value 470.852862
## iter 80 value 420.968463
## iter 90 value 388.626697
## iter 100 value 364.594603
## final value 364.594603
## stopped after 100 iterations
## # weights: 67
## initial value 785.328443
## iter 10 value 517.106499
## iter 20 value 517.036239
## iter 30 value 516.813894
## iter 40 value 516.229945
## iter 50 value 516.225803
## final value 516.225495
## converged
## # weights: 109
## initial value 780.941533
## iter 10 value 519.248915
## iter 20 value 518.724060
## iter 30 value 516.385781
## iter 40 value 515.762571
## iter 50 value 474.013165
## iter 60 value 461.420217
## iter 70 value 433.024942
## iter 80 value 381.511198
## iter 90 value 358.696117
## iter 100 value 352.632151
## final value 352.632151
## stopped after 100 iterations
## # weights: 151
## initial value 630.986453
## iter 10 value 519.009516
## iter 20 value 515.501884
## iter 30 value 515.326981
## iter 40 value 515.106471
## iter 50 value 515.094940
## final value 515.093983
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 43
## initial value 592.624304
## iter 10 value 506.445511
## final value 506.352625
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 63
## initial value 522.918205
## iter 10 value 506.352877
## iter 20 value 506.287501
## iter 30 value 504.337059
## iter 40 value 465.479060
## iter 50 value 426.142158
## iter 60 value 406.058402
## iter 70 value 361.457824
## iter 80 value 350.644134
## iter 90 value 335.546516
## iter 100 value 311.954631
## final value 311.954631
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 103
## initial value 533.611961
## iter 10 value 506.355731
## iter 20 value 506.352608
## iter 30 value 506.321714
## iter 40 value 506.320316
## iter 50 value 506.295759
## iter 60 value 506.289469
## final value 506.289402
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 143
## initial value 602.648084
## iter 10 value 506.305119
## iter 20 value 506.300949
## iter 30 value 506.289314
## iter 40 value 506.281532
## final value 506.281494
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 43
## initial value 577.371850
## iter 10 value 507.025518
## iter 20 value 506.422789
## iter 30 value 506.415728
## final value 506.415651
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 63
## initial value 523.857250
## iter 10 value 506.607468
## iter 20 value 506.417854
## iter 30 value 506.412553
## iter 40 value 490.671958
## iter 50 value 437.410940
## iter 60 value 381.893002
## iter 70 value 356.975774
## iter 80 value 337.311914
## iter 90 value 320.847730
## iter 100 value 318.316511
## final value 318.316511
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 103
## initial value 751.466904
## iter 10 value 505.968304
## iter 20 value 500.204697
## iter 30 value 447.764036
## iter 40 value 399.670692
## iter 50 value 359.643250
## iter 60 value 343.032055
## iter 70 value 315.140813
## iter 80 value 304.723066
## iter 90 value 304.351706
## iter 100 value 303.194645
## final value 303.194645
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 143
## initial value 812.708089
## iter 10 value 507.863844
## iter 20 value 506.497155
## iter 30 value 506.394719
## iter 40 value 506.135864
## iter 50 value 498.307881
## iter 60 value 376.874603
## iter 70 value 350.682040
## iter 80 value 343.348693
## iter 90 value 337.429689
## iter 100 value 329.315612
## final value 329.315612
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 43
## initial value 563.895788
## iter 10 value 506.479470
## iter 20 value 506.478574
## iter 20 value 506.478570
## iter 20 value 506.478567
## final value 506.478567
## converged
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 63
## initial value 539.034003
## iter 10 value 506.691648
## iter 20 value 506.433976
## iter 30 value 506.167183
## iter 40 value 504.245710
## iter 50 value 402.140111
## iter 60 value 365.989207
## iter 70 value 346.712200
## iter 80 value 342.075311
## iter 90 value 336.326709
## iter 100 value 330.765984
## final value 330.765984
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 103
## initial value 530.274616
## iter 10 value 507.893497
## iter 20 value 438.523142
## iter 30 value 371.934126
## iter 40 value 352.305405
## iter 50 value 344.875949
## iter 60 value 335.978356
## iter 70 value 332.061646
## iter 80 value 328.894661
## iter 90 value 325.773617
## iter 100 value 320.619329
## final value 320.619329
## stopped after 100 iterations
## Warning in nnet.formula(.outcome ~ ., data = dat, size = param$size, decay =
## param$decay, : group 'HOROZ' is empty
## # weights: 143
## initial value 552.815879
## iter 10 value 506.489788
## iter 20 value 506.344482
## iter 30 value 445.170035
## iter 40 value 357.714153
## iter 50 value 338.005072
## iter 60 value 335.729750
## iter 70 value 335.619727
## iter 80 value 335.419174
## iter 90 value 333.519543
## iter 100 value 327.835206
## final value 327.835206
## stopped after 100 iterations
## # weights: 151
## initial value 1389.993701
## iter 10 value 770.781391
## iter 20 value 768.638840
## iter 30 value 768.613223
## iter 40 value 768.610859
## iter 50 value 768.565151
## iter 60 value 768.535425
## iter 70 value 768.514304
## iter 80 value 767.855049
## iter 90 value 695.134118
## iter 100 value 633.531357
## final value 633.531357
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n5_NN1Fit0
## Neural Network
##
## 774 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 517, 516, 515
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa
## 2 0.3 0.6151838 0.15382628
## 2 0.5 0.6227447 0.16491956
## 2 0.7 0.6591014 0.31877222
## 3 0.3 0.5594621 0.05978913
## 3 0.5 0.6048677 0.20298822
## 3 0.7 0.6048577 0.20290226
## 5 0.3 0.6668433 0.30712130
## 5 0.5 0.6552903 0.36510800
## 5 0.7 0.6565272 0.36617264
## 7 0.3 0.6681453 0.30387087
## 7 0.5 0.6526563 0.34906882
## 7 0.7 0.5931946 0.18279146
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 7 and decay = 0.3.
DryBean_TDA_KDE_5.60.5_n5_NN1Fit0$resample
## Accuracy Kappa Resample
## 1 0.7325581 0.4899725 Fold2
## 2 0.7081712 0.4216401 Fold1
## 3 0.5637066 0.0000000 Fold3
nb_tda_kde_5.60.5_n5_nn1_fit_re<-DryBean_TDA_KDE_5.60.5_n5_NN1Fit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n5_NN1Fit0)
## a 16-7-4 network with 151 weights
## options were - softmax modelling decay=0.3
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1 i9->h1
## 0.00 -0.03 0.00 0.00 0.00 0.00 0.00 -0.03 0.00 0.00
## i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2 i9->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3 i9->h3
## 0.00 0.04 0.02 -1.61 -0.17 0.02 -0.02 -0.02 -0.61 -0.04
## i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3
## 0.00 -0.01 0.00 0.00 0.00 0.01 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4 i9->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5 i9->h5
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6 i9->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7 i9->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## b->o1 h1->o1 h2->o1 h3->o1 h4->o1 h5->o1 h6->o1 h7->o1
## 0.36 0.36 0.36 -0.71 0.36 0.36 0.36 0.36
## b->o2 h1->o2 h2->o2 h3->o2 h4->o2 h5->o2 h6->o2 h7->o2
## -0.67 -0.67 -0.67 -0.84 -0.67 -0.67 -0.67 -0.67
## b->o3 h1->o3 h2->o3 h3->o3 h4->o3 h5->o3 h6->o3 h7->o3
## 0.06 0.06 0.06 2.12 0.06 0.06 0.06 0.06
## b->o4 h1->o4 h2->o4 h3->o4 h4->o4 h5->o4 h6->o4 h7->o4
## 0.25 0.25 0.25 -0.57 0.25 0.25 0.25 0.25
#vip(DryBean_TDA_KDE_5.60.5_n5_NN1Fit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n5_NN1Fit TDA-Assited NN")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n5_NN1Fit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n5_NN1Fit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n5_db_nn1_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n5_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 0 0 0 1011 173 68 161
## HOROZ 0 0 0 0 0 0 0
## SEKER 396 156 489 52 405 540 629
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3801
## 95% CI : (0.3652, 0.3952)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.237
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9511
## Specificity 1.00000 1.00000 1.0000 0.8668
## Pos Pred Value NaN NaN NaN 0.7155
## Neg Pred Value 0.90294 0.96176 0.8801 0.9805
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2478
## Detection Prevalence 0.00000 0.00000 0.0000 0.3463
## Balanced Accuracy 0.50000 0.50000 0.5000 0.9089
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.8882 0.0000
## Specificity 1.0000 0.3874 1.0000
## Pos Pred Value NaN 0.2025 NaN
## Neg Pred Value 0.8583 0.9519 0.8064
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1324 0.0000
## Detection Prevalence 0.0000 0.6537 0.0000
## Balanced Accuracy 0.5000 0.6378 0.5000
nb_tda_kde_5.60.5_n5_db_nn1_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 0 0 0 1011 173 68 161
## HOROZ 0 0 0 0 0 0 0
## SEKER 396 156 489 52 405 540 629
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3801
## 95% CI : (0.3652, 0.3952)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.237
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9511
## Specificity 1.00000 1.00000 1.0000 0.8668
## Pos Pred Value NaN NaN NaN 0.7155
## Neg Pred Value 0.90294 0.96176 0.8801 0.9805
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2478
## Detection Prevalence 0.00000 0.00000 0.0000 0.3463
## Balanced Accuracy 0.50000 0.50000 0.5000 0.9089
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.8882 0.0000
## Specificity 1.0000 0.3874 1.0000
## Pos Pred Value NaN 0.2025 NaN
## Neg Pred Value 0.8583 0.9519 0.8064
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1324 0.0000
## Detection Prevalence 0.0000 0.6537 0.0000
## Balanced Accuracy 0.5000 0.6378 0.5000
nb_tda_kde_5.60.5_n5_db_nn1_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.801471e-01 2.369712e-01 3.652199e-01 3.952461e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 6.052192e-63 NaN
nb_tda_kde_5.60.5_n5_db_nn1_cf0_ov_acc<-nb_tda_kde_5.60.5_n5_db_nn1_cf0$overall[1]
nb_tda_kde_5.60.5_n5_db_nn1_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9510818 0.8667551 0.7154989 0.9805024 0.7154989
## Class: HOROZ 0.0000000 1.0000000 NaN 0.8583333 NA
## Class: SEKER 0.8881579 0.3873848 0.2024747 0.9518754 0.2024747
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.0000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.0000000
## Class: CALI 0.0000000 NA 0.11985294 0.0000000
## Class: DERMASON 0.9510818 0.8166397 0.26053922 0.2477941
## Class: HOROZ 0.0000000 NA 0.14166667 0.0000000
## Class: SEKER 0.8881579 0.3297710 0.14901961 0.1323529
## Class: SIRA 0.0000000 NA 0.19362745 0.0000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.0000000 0.5000000
## Class: DERMASON 0.3463235 0.9089184
## Class: HOROZ 0.0000000 0.5000000
## Class: SEKER 0.6536765 0.6377713
## Class: SIRA 0.0000000 0.5000000
nb_tda_kde_5.60.5_n5_db_nn1_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n5_db_nn1_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold<-(db_nn1_fit_re - nb_tda_kde_5.60.5_n5_nn1_fit_re)
diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold
## Accuracy
## 1 -0.25821127
## 2 0.03120556
## 3 -0.30339810
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n5_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.25
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n5_3_fold_odds.left<-bst_tda_kde_5.60.5_nn1.n5_3_fold$probLeft/bst_tda_kde_5.60.5_nn1.n5_3_fold$probRight
bst_tda_kde_5.60.5_nn1.n5_3_fold_odds.left
## [1] 2
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n5_3_fold
## $winLeft
## [1] 0.8743
##
## $winRope
## [1] 0.01496667
##
## $winRight
## [1] 0.1107333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n5_3_fold
## $left
## [1] 0.8489567
##
## $rope
## [1] 0.01968622
##
## $right
## [1] 0.1313571
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold))
#bf_tda_kde_5.60.5_nn1.n5_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nn1_n5_3_fold)
## t = -1.6867, df = 2, p-value = 0.2337
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6277978 0.2741952
## sample estimates:
## mean of x
## -0.1768013
### Test set diff
diff_drybean_tda_kde_5.60.5_nn1.n5_test<-(db_nn1_cf_ov_acc - nb_tda_kde_5.60.5_n5_db_nn1_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nn1.n5_test
## Accuracy
## -0.002205882
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n5_test),-0.01,0.01)
bst_tda_kde_5.60.5_nn1.n5_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 1
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nn1.n5_test_odds.left<-bst_tda_kde_5.60.5_nn1.n5_test$probLeft/bst_tda_kde_5.60.5_nn1.n5_test$probRight
bst_tda_kde_5.60.5_nn1.n5_test_odds.left
## [1] NaN
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nn1.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n4_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nn1.n4_test
## $winLeft
## [1] 0.5433333
##
## $winRope
## [1] 0.4566667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nn1.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nn1.n5_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nn1.n5_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nn1.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n5_test)) #bf_tda_pca_5.60.5_nn1.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nn1.n5_test))
##Logistic Regression method='multinom'
dryBeanLrFit <- train(as.factor(Class) ~ .,
data = Dry_Bean_DatasetTrain,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 126 (102 variable)
## initial value 12362.367177
## iter 10 value 9345.294780
## iter 20 value 7164.490167
## iter 30 value 5143.966685
## iter 40 value 2637.302630
## iter 50 value 1416.129507
## iter 60 value 1296.901890
## iter 70 value 1277.016967
## iter 80 value 1256.076175
## iter 90 value 1250.634616
## iter 100 value 1245.972947
## final value 1245.972947
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12362.367177
## iter 10 value 9345.294820
## iter 20 value 7164.492258
## iter 30 value 5144.888013
## iter 40 value 2711.347651
## iter 50 value 1765.814169
## iter 60 value 1626.412826
## iter 70 value 1535.518315
## iter 80 value 1480.661820
## iter 90 value 1462.317743
## iter 100 value 1452.306177
## final value 1452.306177
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12362.367177
## iter 10 value 9345.294780
## iter 20 value 7164.490169
## iter 30 value 5143.967032
## iter 40 value 2637.381736
## iter 50 value 1417.847737
## iter 60 value 1302.024189
## iter 70 value 1283.259665
## iter 80 value 1265.248521
## iter 90 value 1261.203665
## iter 100 value 1257.682293
## final value 1257.682293
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12364.313087
## iter 10 value 9357.236856
## iter 20 value 6855.263499
## iter 30 value 4728.060278
## iter 40 value 2172.226955
## iter 50 value 1351.301105
## iter 60 value 1247.460518
## iter 70 value 1228.221289
## iter 80 value 1213.992099
## iter 90 value 1206.924245
## iter 100 value 1201.728833
## final value 1201.728833
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12364.313087
## iter 10 value 9357.236892
## iter 20 value 6855.265377
## iter 30 value 4728.887783
## iter 40 value 2907.595861
## iter 50 value 1689.292071
## iter 60 value 1563.696402
## iter 70 value 1465.106390
## iter 80 value 1414.589536
## iter 90 value 1396.632628
## iter 100 value 1387.267842
## final value 1387.267842
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12364.313087
## iter 10 value 9357.236856
## iter 20 value 6855.263501
## iter 30 value 4728.060846
## iter 40 value 2172.354110
## iter 50 value 1352.877009
## iter 60 value 1252.057759
## iter 70 value 1234.443552
## iter 80 value 1222.160246
## iter 90 value 1216.967607
## iter 100 value 1213.287790
## final value 1213.287790
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12366.258997
## iter 10 value 9313.217012
## iter 20 value 6781.536950
## iter 30 value 4901.807555
## iter 40 value 2808.689082
## iter 50 value 1412.350788
## iter 60 value 1314.663294
## iter 70 value 1291.256010
## iter 80 value 1268.780063
## iter 90 value 1258.288013
## iter 100 value 1252.420938
## final value 1252.420938
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12366.258997
## iter 10 value 9313.217054
## iter 20 value 6781.538729
## iter 30 value 4902.147789
## iter 40 value 2917.749030
## iter 50 value 1719.610167
## iter 60 value 1596.046512
## iter 70 value 1505.166306
## iter 80 value 1456.063534
## iter 90 value 1439.760310
## iter 100 value 1429.239617
## final value 1429.239617
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 12366.258997
## iter 10 value 9313.217012
## iter 20 value 6781.536948
## iter 30 value 4901.806479
## iter 40 value 2808.778216
## iter 50 value 1413.784935
## iter 60 value 1317.874969
## iter 70 value 1295.832515
## iter 80 value 1276.557913
## iter 90 value 1268.822788
## iter 100 value 1264.497302
## final value 1264.497302
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 18546.469631
## iter 10 value 14825.707034
## iter 20 value 11515.718830
## iter 30 value 7976.721137
## iter 40 value 4178.679145
## iter 50 value 2052.165749
## iter 60 value 1934.406466
## iter 70 value 1900.891657
## iter 80 value 1880.077018
## iter 90 value 1873.024406
## iter 100 value 1866.696046
## final value 1866.696046
## stopped after 100 iterations
dryBeanLrFit
## Penalized Multinomial Regression
##
## 9531 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 6353, 6354, 6355
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9262415 0.9108151
## 1e-04 0.9260314 0.9105678
## 1e-01 0.9223590 0.9061306
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
dryBeanLrFit$resample
## Accuracy Kappa Resample
## 1 0.9310453 0.9166419 Fold3
## 2 0.9241423 0.9082330 Fold2
## 3 0.9235368 0.9075704 Fold1
db_lr_fit_re<-dryBeanLrFit$resample[1]
summary(dryBeanLrFit)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 6.823199 0.003321315 -0.03476634 0.5953995 1.611803
## CALI 32.464501 0.003449109 -0.17486907 1.9678534 2.698588
## DERMASON 25.857735 0.006480290 0.20707237 0.8242785 1.772622
## HOROZ 15.493026 0.007749273 0.08848830 2.3982504 4.323559
## SEKER -21.199426 0.009912825 0.15771817 -1.6360872 -3.063281
## SIRA 73.193155 0.004788652 -0.38557142 2.0281782 2.924417
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 54.039681 12.24680 -0.001456419 -2.787972 -17.3062579
## CALI -62.288895 119.72644 -0.003444130 -4.001427 -0.2896988
## DERMASON 8.273777 78.28066 -0.004122469 -4.429458 -18.3328086
## HOROZ 3.646266 90.57945 -0.006219625 -7.582137 -8.0371112
## SEKER 16.267429 -87.47539 -0.008862278 3.479754 -16.1365558
## SIRA -36.082370 139.77479 -0.004017571 -4.112162 -10.4587654
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 8.671012 17.84284 0.5729655 0.6532757 0.12918310
## CALI 28.031562 -38.31424 0.9351259 0.9700331 0.07548658
## DERMASON 8.298269 139.76078 0.6865460 0.7368440 -0.04588692
## HOROZ 37.949736 69.19092 -20.6370979 1.1388649 -0.17336302
## SEKER -6.119523 91.94795 20.9907986 -0.8258601 0.14420112
## SIRA 47.270315 -141.54437 41.9837921 -0.5919835 -0.21677977
## ShapeFactor3 ShapeFactor4
## BOMBAY -1.668027 9.631844
## CALI -36.413944 -8.096455
## DERMASON -29.052864 7.674724
## HOROZ -57.586298 1.235896
## SEKER 67.380723 16.948915
## SIRA -1.635646 27.525522
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 9.484071e-07 0.0032941561 0.0004238494 0.0001986851 0.0001076132
## CALI 2.858252e-06 0.0003972297 0.0013439211 0.0007146627 0.0005034093
## DERMASON 8.200319e-06 0.0007865185 0.0025773983 0.0017885053 0.0019666050
## HOROZ 3.947037e-06 0.0005063976 0.0018874697 0.0005775069 0.0006034092
## SEKER 4.923735e-06 0.0010253701 0.0021837569 0.0006953062 0.0006443334
## SIRA 6.340917e-06 0.0005818644 0.0021010939 0.0023283420 0.0024089283
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 1.660123e-06 7.903255e-07 0.0032537100 0.0001524253 6.848984e-07
## CALI 5.910173e-06 2.277199e-06 0.0003942677 0.0004144852 2.354548e-06
## DERMASON 1.996073e-05 7.700555e-06 0.0007967773 0.0009055570 7.775731e-06
## HOROZ 5.407329e-06 2.760763e-06 0.0005017110 0.0005625733 3.085227e-06
## SEKER 6.097129e-06 3.182977e-06 0.0010264953 0.0006722176 3.884651e-06
## SIRA 2.486510e-05 8.136608e-06 0.0005835782 0.0008059664 7.034230e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 9.818199e-07 9.676404e-07 7.265826e-07 7.698351e-09 1.652435e-09
## CALI 2.823761e-06 2.781624e-06 2.760759e-06 2.310088e-08 8.914636e-09
## DERMASON 8.107808e-06 9.513165e-06 1.178892e-05 7.147009e-08 6.374348e-08
## HOROZ 3.876048e-06 3.523072e-06 3.630598e-06 3.155145e-08 1.118134e-08
## SEKER 4.881613e-06 4.609670e-06 4.423163e-06 3.993590e-08 1.530821e-08
## SIRA 6.240834e-06 9.305841e-06 1.253697e-05 3.963017e-08 6.690438e-08
## ShapeFactor3 ShapeFactor4
## BOMBAY 5.558518e-07 9.751691e-07
## CALI 2.765547e-06 2.856756e-06
## DERMASON 1.455604e-05 8.209254e-06
## HOROZ 3.317535e-06 3.932212e-06
## SEKER 3.979397e-06 4.911771e-06
## SIRA 1.644605e-05 6.423743e-06
##
## Residual Deviance: 3733.392
## AIC: 3937.392
vip(dryBeanLrFit,25) + ggtitle('non-TDA-Assisted LR')
# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanLrFit, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_lr_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_lr_cf
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 348 0 11 0 1 8 3
## BOMBAY 1 156 0 0 0 0 0
## CALI 29 0 463 0 9 0 1
## DERMASON 0 0 0 978 4 8 65
## HOROZ 2 0 8 3 558 1 12
## SEKER 1 0 1 12 0 572 12
## SIRA 15 0 6 70 6 19 697
##
## Overall Statistics
##
## Accuracy : 0.9245
## 95% CI : (0.916, 0.9324)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.9087
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.87879 1.00000 0.9468 0.9200
## Specificity 0.99376 0.99975 0.9891 0.9745
## Pos Pred Value 0.93801 0.99363 0.9223 0.9270
## Neg Pred Value 0.98706 1.00000 0.9927 0.9719
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08529 0.03824 0.1135 0.2397
## Detection Prevalence 0.09093 0.03848 0.1230 0.2586
## Balanced Accuracy 0.93627 0.99987 0.9680 0.9473
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9654 0.9408 0.8823
## Specificity 0.9926 0.9925 0.9647
## Pos Pred Value 0.9555 0.9565 0.8573
## Neg Pred Value 0.9943 0.9897 0.9715
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1368 0.1402 0.1708
## Detection Prevalence 0.1431 0.1466 0.1993
## Balanced Accuracy 0.9790 0.9667 0.9235
db_lr_cf$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.9245098 0.9087052 0.9159732 0.9324324 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_lr_cf_ov_acc<-db_lr_cf$overall[1]
db_lr_cf$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8787879 0.9937568 0.9380054 0.9870585 0.9380054
## Class: BOMBAY 1.0000000 0.9997452 0.9936306 1.0000000 0.9936306
## Class: CALI 0.9468303 0.9891395 0.9223108 0.9927334 0.9223108
## Class: DERMASON 0.9200376 0.9744780 0.9270142 0.9719008 0.9270142
## Class: HOROZ 0.9653979 0.9925757 0.9554795 0.9942792 0.9554795
## Class: SEKER 0.9407895 0.9925115 0.9565217 0.9896611 0.9565217
## Class: SIRA 0.8822785 0.9647416 0.8573186 0.9715335 0.8573186
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8787879 0.9074316 0.09705882 0.08529412
## Class: BOMBAY 1.0000000 0.9968051 0.03823529 0.03823529
## Class: CALI 0.9468303 0.9344097 0.11985294 0.11348039
## Class: DERMASON 0.9200376 0.9235127 0.26053922 0.23970588
## Class: HOROZ 0.9653979 0.9604131 0.14166667 0.13676471
## Class: SEKER 0.9407895 0.9485904 0.14901961 0.14019608
## Class: SIRA 0.8822785 0.8696195 0.19362745 0.17083333
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09093137 0.9362723
## Class: BOMBAY 0.03848039 0.9998726
## Class: CALI 0.12303922 0.9679849
## Class: DERMASON 0.25857843 0.9472578
## Class: HOROZ 0.14313725 0.9789868
## Class: SEKER 0.14656863 0.9666505
## Class: SIRA 0.19926471 0.9235101
db_lr_cf_pre_rec_f1<-db_lr_cf$byClass[5:7]
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_PC_5.60.5_n1_LrFit0 <- multinom(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n1.vec, family = 'binomial')
## # weights: 108 (85 variable)
## initial value 12246.675972
## iter 10 value 2950.028567
## iter 20 value 2667.760154
## iter 30 value 2184.813614
## iter 40 value 1650.440764
## iter 50 value 1600.491312
## iter 60 value 1584.632550
## iter 70 value 1576.015397
## iter 80 value 1571.201138
## iter 90 value 1562.079765
## iter 100 value 1557.179800
## final value 1557.179800
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n1_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_dry_bean_dataset_5.60.5.n1.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 108 (85 variable)
## initial value 8165.047901
## iter 10 value 2084.191632
## iter 20 value 1892.856219
## iter 30 value 1543.530132
## iter 40 value 1127.191149
## iter 50 value 1079.353011
## iter 60 value 1062.241923
## iter 70 value 1059.374714
## iter 80 value 1057.580615
## iter 90 value 1054.162633
## iter 100 value 1051.729709
## final value 1051.729709
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8165.047901
## iter 10 value 2084.196518
## iter 20 value 1892.905879
## iter 30 value 1454.729612
## iter 40 value 1150.163087
## iter 50 value 1142.828924
## iter 60 value 1142.493226
## iter 70 value 1142.337746
## iter 80 value 1142.328032
## final value 1142.327819
## converged
## # weights: 108 (85 variable)
## initial value 8165.047901
## iter 10 value 2084.191637
## iter 20 value 1892.856239
## iter 30 value 1543.573552
## iter 40 value 1127.313596
## iter 50 value 1080.702754
## iter 60 value 1065.106340
## iter 70 value 1062.639910
## iter 80 value 1061.124951
## iter 90 value 1058.688297
## iter 100 value 1057.269924
## final value 1057.269924
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8163.256142
## iter 10 value 2407.574411
## iter 20 value 2238.090435
## iter 30 value 1406.124391
## iter 40 value 1130.326248
## iter 50 value 1086.169805
## iter 60 value 1074.406881
## iter 70 value 1069.387211
## iter 80 value 1066.277177
## iter 90 value 1057.978686
## iter 100 value 1053.852439
## final value 1053.852439
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8163.256142
## iter 10 value 2407.578523
## iter 20 value 2238.102061
## iter 30 value 1429.421290
## iter 40 value 1155.721672
## iter 50 value 1150.682773
## iter 60 value 1149.587772
## iter 70 value 1149.445229
## iter 80 value 1149.428696
## final value 1149.428566
## converged
## # weights: 108 (85 variable)
## initial value 8163.256142
## iter 10 value 2407.574415
## iter 20 value 2238.090523
## iter 30 value 1406.156914
## iter 40 value 1130.466728
## iter 50 value 1087.791095
## iter 60 value 1077.275585
## iter 70 value 1073.051370
## iter 80 value 1070.539921
## iter 90 value 1065.333604
## iter 100 value 1063.156257
## final value 1063.156257
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8165.047901
## iter 10 value 3157.095970
## iter 20 value 2711.673451
## iter 30 value 1953.040391
## iter 40 value 1136.525804
## iter 50 value 1067.285079
## iter 60 value 1026.614568
## iter 70 value 1011.131999
## iter 80 value 1006.393715
## iter 90 value 1002.760894
## iter 100 value 997.931923
## final value 997.931923
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8165.047901
## iter 10 value 3157.099193
## iter 20 value 2711.703482
## iter 30 value 1632.292787
## iter 40 value 1125.933796
## iter 50 value 1099.597530
## iter 60 value 1096.445647
## iter 70 value 1095.559224
## iter 80 value 1095.381227
## iter 90 value 1095.367646
## final value 1095.367592
## converged
## # weights: 108 (85 variable)
## initial value 8165.047901
## iter 10 value 3157.095973
## iter 20 value 2711.673516
## iter 30 value 1953.125948
## iter 40 value 1136.523086
## iter 50 value 1067.494533
## iter 60 value 1028.832702
## iter 70 value 1015.294342
## iter 80 value 1011.271586
## iter 90 value 1008.585638
## iter 100 value 1005.290408
## final value 1005.290408
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 12246.675972
## iter 10 value 2950.028567
## iter 20 value 2667.760154
## iter 30 value 2184.813614
## iter 40 value 1650.440764
## iter 50 value 1600.491312
## iter 60 value 1584.632550
## iter 70 value 1576.015397
## iter 80 value 1571.201138
## iter 90 value 1562.079765
## iter 100 value 1557.179800
## final value 1557.179800
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n1_LrFit0
## Penalized Multinomial Regression
##
## 6835 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4557, 4556, 4557
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9092901 0.8517882
## 1e-04 0.9087051 0.8508882
## 1e-01 0.9008051 0.8375440
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
DryBean_TDA_PC_5.60.5_n1_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.9091308 0.8514165 Fold3
## 2 0.9113646 0.8554961 Fold2
## 3 0.9073749 0.8484520 Fold1
db_tda_pc_5.60.5_n1_lr_fit_re<-DryBean_TDA_PC_5.60.5_n1_LrFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n1_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 14.22785 0.012357549 0.03548497 -4.4483928 -5.598820
## DERMASON 14.70169 -0.002455073 0.21859390 0.9723484 0.886382
## HOROZ -14.19871 -0.009248312 -0.28194342 0.6316441 0.839296
## SEKER -31.02954 0.004948707 0.24538583 -1.9284924 -3.936744
## SIRA 59.30604 -0.005368799 -0.46478210 1.8912031 2.276575
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI 41.767866 -0.1332494 -0.010302694 9.1461559 -45.18848
## DERMASON -27.812043 46.9011802 0.003411481 -3.3145706 -22.00376
## HOROZ 1.385520 13.7789013 0.004734158 0.7506824 -16.43509
## SEKER 6.274738 -118.7664343 -0.004085662 4.4342313 -19.73911
## SIRA -67.700724 142.8103097 0.005218742 -2.7288513 -12.40141
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 14.26732 3.780069 10.83343 0.3986064 0.08005657
## DERMASON -18.46259 156.328935 11.56775 0.5912817 0.40809953
## HOROZ -13.87012 -20.471940 -25.37286 -0.1727268 -0.28151570
## SEKER -16.76536 130.032845 17.99920 -1.2798091 0.04087314
## SIRA 75.98162 -162.203474 32.68422 0.9047131 0.17606113
## ShapeFactor3 ShapeFactor4
## CALI 10.661592 16.791499
## DERMASON -2.138718 -2.818280
## HOROZ -34.344935 -18.489947
## SEKER 72.401117 7.929439
## SIRA -7.152719 24.915846
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 5.459956e-07 0.0002815565 0.0003022780 9.028743e-05 4.800852e-05
## DERMASON 6.179195e-06 0.0013048137 0.0010980873 2.300130e-03 2.528687e-03
## HOROZ 1.945524e-07 0.0002458235 0.0001007373 5.081088e-05 1.037292e-05
## SEKER 3.439748e-06 0.0011401299 0.0014249072 5.437337e-04 3.980306e-04
## SIRA 5.095434e-06 0.0012753997 0.0009710998 2.210241e-03 2.290754e-03
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI 8.548467e-07 4.394388e-07 0.0002772149 6.662733e-05 3.840218e-07
## DERMASON 2.538859e-05 1.000925e-05 0.0012855380 6.786378e-04 6.455734e-06
## HOROZ 5.921749e-07 2.435506e-07 0.0002433418 2.058792e-05 1.090651e-07
## SEKER 5.289977e-06 2.919095e-06 0.0011241735 4.016197e-04 2.697352e-06
## SIRA 2.441907e-05 9.262902e-06 0.0012553742 5.771301e-04 5.531664e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 5.316135e-07 3.518602e-07 4.316824e-07 5.220538e-09 1.408407e-09
## DERMASON 6.105460e-06 9.695235e-06 1.385512e-05 3.593753e-08 8.256440e-08
## HOROZ 1.829085e-07 9.532507e-08 1.000163e-07 2.824102e-09 3.335774e-10
## SEKER 3.409556e-06 3.349817e-06 3.153934e-06 3.291227e-08 1.432030e-08
## SIRA 5.034704e-06 8.169101e-06 1.219814e-05 3.600609e-08 7.227987e-08
## ShapeFactor3 ShapeFactor4
## CALI 3.386087e-07 5.443332e-07
## DERMASON 1.878599e-05 6.188195e-06
## HOROZ 7.505122e-08 1.898514e-07
## SEKER 3.090575e-06 3.435948e-06
## SIRA 1.673332e-05 5.103272e-06
##
## Residual Deviance: 3114.36
## AIC: 3284.36
vip(DryBean_TDA_PC_5.60.5_n1_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.60.5_n1_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_PC_5.60.5_n1_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n1_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n1_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n1_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 68 0 23 0 0 3 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 13 117 1 0 0 0 0
## DERMASON 8 4 6 994 414 8 77
## HOROZ 0 0 0 0 0 0 0
## SEKER 273 35 120 13 3 579 13
## SIRA 34 0 339 56 161 18 700
##
## Overall Statistics
##
## Accuracy : 0.574
## 95% CI : (0.5587, 0.5893)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4659
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.17172 0.00000 0.0020450 0.9351
## Specificity 0.99294 1.00000 0.9637984 0.8286
## Pos Pred Value 0.72340 NaN 0.0076336 0.6578
## Neg Pred Value 0.91771 0.96176 0.8764244 0.9731
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.01667 0.00000 0.0002451 0.2436
## Detection Prevalence 0.02304 0.00000 0.0321078 0.3703
## Balanced Accuracy 0.58233 0.50000 0.4829217 0.8819
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9523 0.8861
## Specificity 1.0000 0.8684 0.8152
## Pos Pred Value NaN 0.5589 0.5352
## Neg Pred Value 0.8583 0.9905 0.9675
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1419 0.1716
## Detection Prevalence 0.0000 0.2539 0.3206
## Balanced Accuracy 0.5000 0.9103 0.8506
db_tda_pc_5.60.5_n1_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 68 0 23 0 0 3 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 13 117 1 0 0 0 0
## DERMASON 8 4 6 994 414 8 77
## HOROZ 0 0 0 0 0 0 0
## SEKER 273 35 120 13 3 579 13
## SIRA 34 0 339 56 161 18 700
##
## Overall Statistics
##
## Accuracy : 0.574
## 95% CI : (0.5587, 0.5893)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4659
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.17172 0.00000 0.0020450 0.9351
## Specificity 0.99294 1.00000 0.9637984 0.8286
## Pos Pred Value 0.72340 NaN 0.0076336 0.6578
## Neg Pred Value 0.91771 0.96176 0.8764244 0.9731
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.01667 0.00000 0.0002451 0.2436
## Detection Prevalence 0.02304 0.00000 0.0321078 0.3703
## Balanced Accuracy 0.58233 0.50000 0.4829217 0.8819
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.0000 0.9523 0.8861
## Specificity 1.0000 0.8684 0.8152
## Pos Pred Value NaN 0.5589 0.5352
## Neg Pred Value 0.8583 0.9905 0.9675
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.0000 0.1419 0.1716
## Detection Prevalence 0.0000 0.2539 0.3206
## Balanced Accuracy 0.5000 0.9103 0.8506
db_tda_pc_5.60.5_n1_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5740196 0.4658638 0.5586754 0.5892577 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n1_db_lr_cf0_ov_acc<-db_tda_pc_5.60.5_n1_db_lr_cf0$overall[1]
db_tda_pc_5.60.5_n1_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value
## Class: BARBUNYA 0.17171717 0.9929425 0.723404255 0.9177120
## Class: BOMBAY 0.00000000 1.0000000 NaN 0.9617647
## Class: CALI 0.00204499 0.9637984 0.007633588 0.8764244
## Class: DERMASON 0.93508937 0.8286377 0.657842488 0.9731413
## Class: HOROZ 0.00000000 1.0000000 NaN 0.8583333
## Class: SEKER 0.95230263 0.8683756 0.558880309 0.9904731
## Class: SIRA 0.88607595 0.8151976 0.535168196 0.9675325
## Precision Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.723404255 0.17171717 0.277551020 0.09705882 0.016666667
## Class: BOMBAY NA 0.00000000 NA 0.03823529 0.000000000
## Class: CALI 0.007633588 0.00204499 0.003225806 0.11985294 0.000245098
## Class: DERMASON 0.657842488 0.93508937 0.772338772 0.26053922 0.243627451
## Class: HOROZ NA 0.00000000 NA 0.14166667 0.000000000
## Class: SEKER 0.558880309 0.95230263 0.704379562 0.14901961 0.141911765
## Class: SIRA 0.535168196 0.88607595 0.667302193 0.19362745 0.171568627
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.02303922 0.5823298
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.03210784 0.4829217
## Class: DERMASON 0.37034314 0.8818635
## Class: HOROZ 0.00000000 0.5000000
## Class: SEKER 0.25392157 0.9103391
## Class: SIRA 0.32058824 0.8506368
db_tda_pc_5.60.5_n1_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n1_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_lr_n1_3_fold<-(db_lr_fit_re - db_tda_pc_5.60.5_n1_lr_fit_re)
diff_drybean_tda_pca_5.60.5_lr_n1_3_fold
## Accuracy
## 1 0.02191452
## 2 0.01277764
## 3 0.01616193
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n1_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n1_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0903
##
## $winRight
## [1] 0.9097
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n1_3_fold
## $left
## [1] 0.006402891
##
## $rope
## [1] 0.06988669
##
## $right
## [1] 0.9237104
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold))
#bf_tda_pca_5.60.5_lr.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_lr_n1_3_fold)
## t = 6.3561, df = 2, p-value = 0.02387
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.005476347 0.028426378
## sample estimates:
## mean of x
## 0.01695136
### Test set diff
diff_drybean_tda_pca_5.60.5_lr.n1_test<-(db_lr_cf_ov_acc - db_tda_pc_5.60.5_n1_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_lr.n1_test
## Accuracy
## 0.3504902
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n1_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n1_test$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n1_test$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1616333
##
## $winRight
## [1] 0.8383667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_lr.n1_test)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr.n1_test)) #bf_tda_pca_5.60.5_lr.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n1_test))
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node2
DryBean_TDA_PC_5.60.5_n2_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_dry_bean_dataset_5.60.5.n2.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 108 (85 variable)
## initial value 9584.121401
## iter 10 value 5499.655931
## iter 20 value 4458.162743
## iter 30 value 3105.827306
## iter 40 value 1657.685974
## iter 50 value 1609.806849
## iter 60 value 1571.065970
## iter 70 value 1553.127350
## iter 80 value 1549.271649
## iter 90 value 1546.388947
## iter 100 value 1544.154888
## final value 1544.154888
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9584.121401
## iter 10 value 5499.656416
## iter 20 value 4458.167602
## iter 30 value 3330.846257
## iter 40 value 1790.971429
## iter 50 value 1705.215957
## iter 60 value 1682.876917
## iter 70 value 1678.731763
## iter 80 value 1678.302318
## iter 90 value 1678.233365
## iter 100 value 1678.221398
## final value 1678.221398
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9584.121401
## iter 10 value 5499.655932
## iter 20 value 4458.162772
## iter 30 value 3106.056700
## iter 40 value 1658.354105
## iter 50 value 1611.301945
## iter 60 value 1575.882214
## iter 70 value 1561.009908
## iter 80 value 1557.989667
## iter 90 value 1555.895117
## iter 100 value 1554.409435
## final value 1554.409435
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9584.121401
## iter 10 value 5588.881570
## iter 20 value 4328.962915
## iter 30 value 2920.240822
## iter 40 value 1672.571403
## iter 50 value 1611.857568
## iter 60 value 1574.108362
## iter 70 value 1541.104264
## iter 80 value 1534.941738
## iter 90 value 1532.408660
## iter 100 value 1530.145851
## final value 1530.145851
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9584.121401
## iter 10 value 5588.881911
## iter 20 value 4328.972491
## iter 30 value 2990.068226
## iter 40 value 1804.489492
## iter 50 value 1701.904225
## iter 60 value 1681.897094
## iter 70 value 1674.158988
## iter 80 value 1673.837777
## iter 90 value 1673.803635
## iter 100 value 1673.800106
## final value 1673.800106
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9584.121401
## iter 10 value 5588.881570
## iter 20 value 4328.962837
## iter 30 value 2920.266218
## iter 40 value 1673.176812
## iter 50 value 1613.256935
## iter 60 value 1578.140430
## iter 70 value 1549.759917
## iter 80 value 1544.827658
## iter 90 value 1542.839816
## iter 100 value 1541.028040
## final value 1541.028040
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9585.913160
## iter 10 value 5642.301786
## iter 20 value 4516.220244
## iter 30 value 2994.521146
## iter 40 value 1675.118344
## iter 50 value 1622.410746
## iter 60 value 1595.366834
## iter 70 value 1566.414674
## iter 80 value 1561.314251
## iter 90 value 1557.511262
## iter 100 value 1554.571364
## final value 1554.571364
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9585.913160
## iter 10 value 5642.302128
## iter 20 value 4516.225948
## iter 30 value 3048.877381
## iter 40 value 1829.963151
## iter 50 value 1720.696625
## iter 60 value 1699.721184
## iter 70 value 1692.898520
## iter 80 value 1692.550231
## iter 90 value 1692.532587
## iter 100 value 1692.529835
## final value 1692.529835
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 9585.913160
## iter 10 value 5642.301787
## iter 20 value 4516.220247
## iter 30 value 2994.573763
## iter 40 value 1675.847582
## iter 50 value 1623.852678
## iter 60 value 1598.692713
## iter 70 value 1573.768811
## iter 80 value 1569.736621
## iter 90 value 1566.549705
## iter 100 value 1564.358332
## final value 1564.358332
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 14377.077981
## iter 10 value 7983.921257
## iter 20 value 5824.210106
## iter 30 value 4819.144952
## iter 40 value 2498.278848
## iter 50 value 2423.505014
## iter 60 value 2368.194615
## iter 70 value 2346.643831
## iter 80 value 2341.303225
## iter 90 value 2338.050676
## iter 100 value 2332.160306
## final value 2332.160306
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n2_LrFit0
## Penalized Multinomial Regression
##
## 8024 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5349, 5349, 5350
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.8884594 0.8544936
## 1e-04 0.8882100 0.8541548
## 1e-01 0.8852194 0.8502247
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
DryBean_TDA_PC_5.60.5_n2_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.8863126 0.8519016 Fold3
## 2 0.8844860 0.8489939 Fold2
## 3 0.8945794 0.8625852 Fold1
db_tda_pc_5.60.5_n2_lr_fit_re<-DryBean_TDA_PC_5.60.5_n2_LrFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n2_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 19.409745 0.003250476 -0.1843568 2.1409408 2.6310521
## DERMASON -5.698789 0.004877043 0.1930933 0.8704822 1.6323456
## HOROZ 12.877483 0.008837950 0.1007845 2.8286058 4.1033595
## SEKER -15.458689 0.005734884 0.2147872 0.2995945 -0.4809507
## SIRA 55.845899 0.003984098 -0.4003069 2.4552142 2.7973113
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI -68.514521 81.17577 -0.004126732 -3.7448698 3.730739
## DERMASON -6.698071 46.58725 -0.006210418 -2.9169298 -16.563219
## HOROZ -46.726333 97.64395 -0.007863913 -7.7231540 -7.808393
## SEKER -45.132989 -25.62675 -0.006622925 -0.4265182 -12.104241
## SIRA -85.227129 122.09640 -0.005791664 -3.4567569 -8.074913
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 19.97221 -48.32506 3.181868 0.41119286 0.06073577
## DERMASON -31.38751 129.63293 -17.748612 0.01000724 -0.27067390
## HOROZ 51.81586 73.28848 -11.521348 -0.09262879 -0.25068627
## SEKER -40.32917 123.09272 12.455957 -0.50130500 0.26665353
## SIRA 46.31845 -156.27956 36.727266 1.06032004 0.24494081
## ShapeFactor3 ShapeFactor4
## CALI -18.2862050 -8.868924
## DERMASON -33.1093020 -20.501120
## HOROZ -41.5232264 4.092994
## SEKER 37.1268348 -10.678281
## SIRA -0.7282607 18.820626
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 3.153291e-06 0.0004296838 0.001515468 0.0007345180 0.0004715310
## DERMASON 8.218137e-06 0.0007808195 0.002708497 0.0015229222 0.0018909087
## HOROZ 4.723093e-06 0.0007228365 0.002114824 0.0008565766 0.0005375649
## SEKER 5.080934e-06 0.0007449363 0.002414981 0.0007347658 0.0005852958
## SIRA 6.090966e-06 0.0006261116 0.002260123 0.0022039847 0.0022181577
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI 6.342994e-06 2.622437e-06 0.0004307586 0.0004484459 2.449513e-06
## DERMASON 1.699380e-05 6.169324e-06 0.0007981367 0.0009024962 8.064631e-06
## HOROZ 8.314309e-06 3.801509e-06 0.0007278660 0.0006423939 3.191821e-06
## SEKER 6.778620e-06 3.557745e-06 0.0007546640 0.0006555003 3.960020e-06
## SIRA 2.329522e-05 7.676767e-06 0.0006359710 0.0007593239 6.691062e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 3.121039e-06 2.835941e-06 2.821441e-06 2.689247e-08 9.038095e-09
## DERMASON 8.120558e-06 8.904618e-06 1.152242e-05 6.652709e-08 5.940042e-08
## HOROZ 4.672319e-06 4.237841e-06 3.761210e-06 4.320281e-08 1.053479e-08
## SEKER 5.022807e-06 4.199672e-06 4.393347e-06 4.424373e-08 1.458596e-08
## SIRA 5.988616e-06 8.456910e-06 1.173750e-05 3.755328e-08 6.305819e-08
## ShapeFactor3 ShapeFactor4
## CALI 2.713365e-06 3.164450e-06
## DERMASON 1.380002e-05 8.236162e-06
## HOROZ 3.077089e-06 4.718149e-06
## SEKER 3.790540e-06 5.078708e-06
## SIRA 1.533446e-05 6.196997e-06
##
## Residual Deviance: 4664.321
## AIC: 4834.321
vip(DryBean_TDA_PC_5.60.5_n2_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.60.5_n2_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_PC_5.60.5_n2_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n2_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n2_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n2_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 354 122 15 1 1 3 4
## BOMBAY 0 0 0 0 0 0 0
## CALI 28 0 446 0 8 0 1
## DERMASON 0 0 0 985 3 18 68
## HOROZ 2 34 21 3 558 1 8
## SEKER 3 0 1 9 0 566 13
## SIRA 9 0 6 65 8 20 696
##
## Overall Statistics
##
## Accuracy : 0.8836
## 95% CI : (0.8733, 0.8933)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8587
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.89394 0.00000 0.9121 0.9266
## Specificity 0.96037 1.00000 0.9897 0.9705
## Pos Pred Value 0.70800 NaN 0.9234 0.9171
## Neg Pred Value 0.98827 0.96176 0.9880 0.9741
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08676 0.00000 0.1093 0.2414
## Detection Prevalence 0.12255 0.00000 0.1184 0.2632
## Balanced Accuracy 0.92715 0.50000 0.9509 0.9486
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9654 0.9309 0.8810
## Specificity 0.9803 0.9925 0.9672
## Pos Pred Value 0.8900 0.9561 0.8657
## Neg Pred Value 0.9942 0.9880 0.9713
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1368 0.1387 0.1706
## Detection Prevalence 0.1537 0.1451 0.1971
## Balanced Accuracy 0.9728 0.9617 0.9241
db_tda_pc_5.60.5_n2_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 354 122 15 1 1 3 4
## BOMBAY 0 0 0 0 0 0 0
## CALI 28 0 446 0 8 0 1
## DERMASON 0 0 0 985 3 18 68
## HOROZ 2 34 21 3 558 1 8
## SEKER 3 0 1 9 0 566 13
## SIRA 9 0 6 65 8 20 696
##
## Overall Statistics
##
## Accuracy : 0.8836
## 95% CI : (0.8733, 0.8933)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8587
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.89394 0.00000 0.9121 0.9266
## Specificity 0.96037 1.00000 0.9897 0.9705
## Pos Pred Value 0.70800 NaN 0.9234 0.9171
## Neg Pred Value 0.98827 0.96176 0.9880 0.9741
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08676 0.00000 0.1093 0.2414
## Detection Prevalence 0.12255 0.00000 0.1184 0.2632
## Balanced Accuracy 0.92715 0.50000 0.9509 0.9486
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9654 0.9309 0.8810
## Specificity 0.9803 0.9925 0.9672
## Pos Pred Value 0.8900 0.9561 0.8657
## Neg Pred Value 0.9942 0.9880 0.9713
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1368 0.1387 0.1706
## Detection Prevalence 0.1537 0.1451 0.1971
## Balanced Accuracy 0.9728 0.9617 0.9241
db_tda_pc_5.60.5_n2_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8835784 0.8586748 0.8733388 0.8932651 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n2_db_lr_cf0_ov_acc<-db_tda_pc_5.60.5_n2_db_lr_cf0$overall[1]
db_tda_pc_5.60.5_n2_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8939394 0.9603692 0.7080000 0.9882682 0.7080000
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9120654 0.9896965 0.9233954 0.9880456 0.9233954
## Class: DERMASON 0.9266228 0.9705005 0.9171322 0.9740519 0.9171322
## Class: HOROZ 0.9653979 0.9802970 0.8899522 0.9942079 0.8899522
## Class: SEKER 0.9309211 0.9925115 0.9560811 0.9879587 0.9560811
## Class: SIRA 0.8810127 0.9671733 0.8656716 0.9713065 0.8656716
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8939394 0.7901786 0.09705882 0.08676471
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.9120654 0.9176955 0.11985294 0.10931373
## Class: DERMASON 0.9266228 0.9218531 0.26053922 0.24142157
## Class: HOROZ 0.9653979 0.9261411 0.14166667 0.13676471
## Class: SEKER 0.9309211 0.9433333 0.14901961 0.13872549
## Class: SIRA 0.8810127 0.8732748 0.19362745 0.17058824
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.1225490 0.9271543
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.1183824 0.9508810
## Class: DERMASON 0.2632353 0.9485616
## Class: HOROZ 0.1536765 0.9728474
## Class: SEKER 0.1450980 0.9617163
## Class: SIRA 0.1970588 0.9240930
db_tda_pc_5.60.5_n2_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n2_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_lr_n2_3_fold<-(db_lr_fit_re - db_tda_pc_5.60.5_n2_lr_fit_re)
diff_drybean_tda_pca_5.60.5_lr_n2_3_fold
## Accuracy
## 1 0.04473270
## 2 0.03965629
## 3 0.02895738
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n2_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n2_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0096
##
## $winRight
## [1] 0.9904
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n2_3_fold
## $left
## [1] 0.006194892
##
## $rope
## [1] 0.01149125
##
## $right
## [1] 0.9823139
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold))
#bf_tda_pca_5.60.5_lr.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_lr_n2_3_fold)
## t = 8.1263, df = 2, p-value = 0.01481
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.01777755 0.05778670
## sample estimates:
## mean of x
## 0.03778212
### Test set diff
diff_drybean_tda_pca_5.60.5_lr.n2_test<-(db_lr_cf_ov_acc - db_tda_pc_5.60.5_n2_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_lr.n2_test
## Accuracy
## 0.04093137
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n2_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n2_test$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n2_test$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1587
##
## $winRight
## [1] 0.8413
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_lr.n2_test)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr.n2_test)) #bf_tda_pca_5.60.5_lr.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n2_test))
##Node3
DryBean_TDA_PC_5.60.5_n3_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_dry_bean_dataset_5.60.5.n3.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'DERMASON' is empty
## # weights: 108 (85 variable)
## initial value 5979.101349
## iter 10 value 3034.372966
## iter 20 value 1972.910199
## iter 30 value 1137.199499
## iter 40 value 540.778137
## iter 50 value 521.497566
## iter 60 value 513.684150
## iter 70 value 510.646121
## iter 80 value 507.225480
## iter 90 value 503.932490
## iter 100 value 502.901038
## final value 502.901038
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'DERMASON' is empty
## # weights: 108 (85 variable)
## initial value 5979.101349
## iter 10 value 3034.373056
## iter 20 value 1972.927169
## iter 30 value 1207.735281
## iter 40 value 614.025645
## iter 50 value 564.297128
## iter 60 value 556.306725
## iter 70 value 553.667913
## iter 80 value 552.870496
## iter 90 value 551.434461
## iter 100 value 550.618992
## final value 550.618992
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'DERMASON' is empty
## # weights: 108 (85 variable)
## initial value 5979.101349
## iter 10 value 3034.372965
## iter 20 value 1972.910191
## iter 30 value 1137.252810
## iter 40 value 541.199981
## iter 50 value 522.170452
## iter 60 value 514.837178
## iter 70 value 512.187467
## iter 80 value 509.640395
## iter 90 value 507.712387
## iter 100 value 507.219068
## final value 507.219068
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 6497.393988
## iter 10 value 3611.367398
## iter 20 value 2782.757750
## iter 30 value 2392.307299
## iter 40 value 805.226361
## iter 50 value 522.418935
## iter 60 value 503.743512
## iter 70 value 495.963211
## iter 80 value 487.471254
## iter 90 value 480.571957
## iter 100 value 476.988806
## final value 476.988806
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 6497.393988
## iter 10 value 3611.367411
## iter 20 value 2782.761229
## iter 30 value 2392.430760
## iter 40 value 816.341105
## iter 50 value 597.278271
## iter 60 value 551.924881
## iter 70 value 538.296627
## iter 80 value 535.537853
## iter 90 value 535.129046
## iter 100 value 534.478572
## final value 534.478572
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 6497.393988
## iter 10 value 3611.367396
## iter 20 value 2782.757398
## iter 30 value 2392.215448
## iter 40 value 824.403681
## iter 50 value 520.222817
## iter 60 value 504.585704
## iter 70 value 497.370053
## iter 80 value 490.728625
## iter 90 value 485.308346
## iter 100 value 483.146108
## final value 483.146108
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'SEKER' is empty
## # weights: 108 (85 variable)
## initial value 5984.476627
## iter 10 value 3107.003016
## iter 20 value 2022.401459
## iter 30 value 1098.756154
## iter 40 value 546.565060
## iter 50 value 516.336049
## iter 60 value 505.718495
## iter 70 value 499.593011
## iter 80 value 489.790266
## iter 90 value 485.999525
## iter 100 value 482.232741
## final value 482.232741
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'SEKER' is empty
## # weights: 108 (85 variable)
## initial value 5984.476627
## iter 10 value 3107.003101
## iter 20 value 2022.409974
## iter 30 value 1149.637349
## iter 40 value 641.109887
## iter 50 value 566.916922
## iter 60 value 557.488085
## iter 70 value 554.953318
## iter 80 value 554.392297
## iter 90 value 553.571533
## iter 100 value 553.159426
## final value 553.159426
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'SEKER' is empty
## # weights: 108 (85 variable)
## initial value 5984.476627
## iter 10 value 3107.003017
## iter 20 value 2022.401502
## iter 30 value 1098.837475
## iter 40 value 546.954658
## iter 50 value 517.012883
## iter 60 value 507.127067
## iter 70 value 501.788626
## iter 80 value 495.029579
## iter 90 value 492.796009
## iter 100 value 490.933113
## final value 490.933113
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9745.118026
## iter 10 value 4517.266615
## iter 20 value 3324.528646
## iter 30 value 3068.370504
## iter 40 value 1210.781359
## iter 50 value 782.151109
## iter 60 value 766.188183
## iter 70 value 754.909120
## iter 80 value 744.747552
## iter 90 value 739.282538
## iter 100 value 736.298164
## final value 736.298164
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n3_LrFit0
## Penalized Multinomial Regression
##
## 5008 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 3337, 3339, 3340
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9452847 0.9207964
## 1e-04 0.9448856 0.9202176
## 1e-01 0.9410931 0.9145963
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.
DryBean_TDA_PC_5.60.5_n3_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.9436451 0.9185310 Fold3
## 2 0.9406830 0.9141842 Fold2
## 3 0.9515260 0.9296741 Fold1
db_tda_pc_5.60.5_n3_lr_fit_re<-DryBean_TDA_PC_5.60.5_n2_LrFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n3_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 14.30163 0.0119320472 -0.044989215 1.400681 2.722384
## CALI 22.66676 0.0029400067 -0.173611175 1.834276 2.695622
## DERMASON 18.91713 0.0051320514 0.008693355 1.488015 2.368055
## HOROZ -14.57903 0.0067383099 0.100545807 1.798648 4.106024
## SEKER 22.37020 0.0031423125 -0.053428700 2.049694 3.378469
## SIRA 19.27074 0.0004244842 -0.015470968 2.206961 1.498233
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 39.57132 16.73072 -0.009883308 -4.703733 1.761246
## CALI -64.63282 134.18415 -0.003374680 -3.631153 4.815713
## DERMASON 12.04918 14.16334 -0.003573557 -4.595545 10.907022
## HOROZ 26.72792 88.26114 -0.006462026 -6.056983 -2.812408
## SEKER 27.61690 10.69756 -0.001150640 -6.109034 7.170042
## SIRA -127.59671 53.41189 -0.003939836 -2.752798 -7.929204
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 11.671991 1.787331 8.556089 0.23812164 0.02412847
## CALI 36.024767 -41.130548 -17.816503 0.40408364 -0.26340126
## DERMASON 15.003534 8.381469 17.315752 0.29755257 0.11808973
## HOROZ 6.304111 77.129026 -57.951286 0.12273240 -0.54395322
## SEKER 13.257169 5.877069 20.392897 0.38131795 0.13038006
## SIRA 8.333177 53.902684 5.922615 -0.01003625 -0.04082324
## ShapeFactor3 ShapeFactor4
## BOMBAY 4.889735 13.88936
## CALI -61.811757 -14.36913
## DERMASON 14.681975 16.24844
## HOROZ -98.532192 -26.39504
## SEKER 18.235966 19.70641
## SIRA -17.035093 -16.15048
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 1.040351e-07 1.526589e-04 5.729836e-05 1.997657e-05 2.445222e-05
## CALI 9.353647e-06 3.104293e-04 1.151684e-03 3.464698e-03 4.101794e-03
## DERMASON 7.810083e-09 6.357547e-05 2.695823e-06 2.053323e-06 4.939265e-07
## HOROZ 4.841449e-06 3.935087e-04 2.063908e-03 2.305130e-03 2.004134e-03
## SEKER 6.915675e-08 5.654433e-05 2.946852e-05 1.084834e-05 6.805963e-06
## SIRA 7.083522e-06 5.782615e-04 3.350225e-03 1.136800e-03 7.153371e-04
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 1.547259e-07 6.412178e-08 1.509108e-04 1.603800e-05 1.033238e-07
## CALI 3.124845e-05 4.607702e-06 3.088814e-04 1.383446e-03 8.806939e-06
## DERMASON 2.155067e-08 8.935071e-09 6.568000e-05 1.042589e-06 4.767306e-09
## HOROZ 2.126979e-05 4.342506e-06 3.908684e-04 6.999337e-04 3.884852e-06
## SEKER 1.154908e-07 5.640751e-08 5.918948e-05 8.679923e-06 4.479719e-08
## SIRA 1.188234e-05 5.716070e-06 5.857954e-04 9.071344e-04 4.835332e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 1.009756e-07 8.387732e-08 1.108370e-07 7.470359e-10 3.315867e-10
## CALI 9.212213e-06 1.508136e-05 1.727763e-05 1.813995e-08 6.443281e-08
## DERMASON 7.967422e-09 1.202887e-08 4.254419e-09 9.520755e-11 8.958808e-12
## HOROZ 4.764342e-06 6.536049e-06 8.412030e-06 3.295465e-08 3.207956e-08
## SEKER 6.849828e-08 5.730269e-08 5.315356e-08 7.057375e-10 1.448740e-10
## SIRA 6.977021e-06 5.474149e-06 5.478494e-06 6.923424e-08 1.462953e-08
## ShapeFactor3 ShapeFactor4
## BOMBAY 1.170527e-07 1.040068e-07
## CALI 2.097073e-05 9.152293e-06
## DERMASON 2.462094e-09 7.750721e-09
## HOROZ 1.030359e-05 4.739947e-06
## SEKER 4.067398e-08 6.903252e-08
## SIRA 4.239731e-06 7.105550e-06
##
## Residual Deviance: 1472.596
## AIC: 1676.596
vip(DryBean_TDA_PC_5.60.5_n3_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.60.5_n3_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_PC_5.60.5_n3_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n3_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n3_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
db_tda_pc_5.60.5_n3_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 358 0 14 2 2 35 9
## BOMBAY 0 147 0 1 0 14 0
## CALI 29 0 462 0 8 0 3
## DERMASON 0 0 0 13 0 2 1
## HOROZ 2 0 9 19 561 0 14
## SEKER 1 9 0 220 0 187 0
## SIRA 6 0 4 808 7 370 763
##
## Overall Statistics
##
## Accuracy : 0.6105
## 95% CI : (0.5954, 0.6255)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5383
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.90404 0.94231 0.9448 0.012230
## Specificity 0.98317 0.99618 0.9889 0.999006
## Pos Pred Value 0.85238 0.90741 0.9203 0.812500
## Neg Pred Value 0.98962 0.99770 0.9925 0.741634
## Prevalence 0.09706 0.03824 0.1199 0.260539
## Detection Rate 0.08775 0.03603 0.1132 0.003186
## Detection Prevalence 0.10294 0.03971 0.1230 0.003922
## Balanced Accuracy 0.94361 0.96924 0.9668 0.505618
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9706 0.30757 0.9658
## Specificity 0.9874 0.93376 0.6368
## Pos Pred Value 0.9273 0.44844 0.3897
## Neg Pred Value 0.9951 0.88507 0.9873
## Prevalence 0.1417 0.14902 0.1936
## Detection Rate 0.1375 0.04583 0.1870
## Detection Prevalence 0.1483 0.10221 0.4799
## Balanced Accuracy 0.9790 0.62066 0.8013
db_tda_pc_5.60.5_n3_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 358 0 14 2 2 35 9
## BOMBAY 0 147 0 1 0 14 0
## CALI 29 0 462 0 8 0 3
## DERMASON 0 0 0 13 0 2 1
## HOROZ 2 0 9 19 561 0 14
## SEKER 1 9 0 220 0 187 0
## SIRA 6 0 4 808 7 370 763
##
## Overall Statistics
##
## Accuracy : 0.6105
## 95% CI : (0.5954, 0.6255)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5383
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.90404 0.94231 0.9448 0.012230
## Specificity 0.98317 0.99618 0.9889 0.999006
## Pos Pred Value 0.85238 0.90741 0.9203 0.812500
## Neg Pred Value 0.98962 0.99770 0.9925 0.741634
## Prevalence 0.09706 0.03824 0.1199 0.260539
## Detection Rate 0.08775 0.03603 0.1132 0.003186
## Detection Prevalence 0.10294 0.03971 0.1230 0.003922
## Balanced Accuracy 0.94361 0.96924 0.9668 0.505618
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9706 0.30757 0.9658
## Specificity 0.9874 0.93376 0.6368
## Pos Pred Value 0.9273 0.44844 0.3897
## Neg Pred Value 0.9951 0.88507 0.9873
## Prevalence 0.1417 0.14902 0.1936
## Detection Rate 0.1375 0.04583 0.1870
## Detection Prevalence 0.1483 0.10221 0.4799
## Balanced Accuracy 0.9790 0.62066 0.8013
db_tda_pc_5.60.5_n3_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.6105392 0.5383142 0.5953792 0.6255407 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n3_db_lr_cf0_ov_acc<-db_tda_pc_5.60.5_n3_db_lr_cf0$overall[1]
db_tda_pc_5.60.5_n3_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.90404040 0.9831705 0.8523810 0.9896175 0.8523810
## Class: BOMBAY 0.94230769 0.9961774 0.9074074 0.9977029 0.9074074
## Class: CALI 0.94478528 0.9888610 0.9203187 0.9924539 0.9203187
## Class: DERMASON 0.01222954 0.9990056 0.8125000 0.7416339 0.8125000
## Class: HOROZ 0.97058824 0.9874358 0.9272727 0.9951079 0.9272727
## Class: SEKER 0.30756579 0.9337558 0.4484412 0.8850669 0.4484412
## Class: SIRA 0.96582278 0.6367781 0.3896834 0.9872762 0.3896834
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.90404040 0.87745098 0.09705882 0.087745098
## Class: BOMBAY 0.94230769 0.92452830 0.03823529 0.036029412
## Class: CALI 0.94478528 0.93239152 0.11985294 0.113235294
## Class: DERMASON 0.01222954 0.02409639 0.26053922 0.003186275
## Class: HOROZ 0.97058824 0.94843618 0.14166667 0.137500000
## Class: SEKER 0.30756579 0.36487805 0.14901961 0.045833333
## Class: SIRA 0.96582278 0.55531295 0.19362745 0.187009804
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.102941176 0.9436054
## Class: BOMBAY 0.039705882 0.9692425
## Class: CALI 0.123039216 0.9668232
## Class: DERMASON 0.003921569 0.5056176
## Class: HOROZ 0.148284314 0.9790120
## Class: SEKER 0.102205882 0.6206608
## Class: SIRA 0.479901961 0.8013005
db_tda_pc_5.60.5_n3_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n3_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_lr_n3_3_fold<-(db_lr_fit_re - db_tda_pc_5.60.5_n3_lr_fit_re)
diff_drybean_tda_pca_5.60.5_lr_n3_3_fold
## Accuracy
## 1 0.04473270
## 2 0.03965629
## 3 0.02895738
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n3_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n3_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n3_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n3_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.009
##
## $winRight
## [1] 0.991
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n3_3_fold
## $left
## [1] 0.006194892
##
## $rope
## [1] 0.01149125
##
## $right
## [1] 0.9823139
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold))
#bf_tda_pca_5.60.5_lr.n3_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_lr_n3_3_fold)
## t = 8.1263, df = 2, p-value = 0.01481
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.01777755 0.05778670
## sample estimates:
## mean of x
## 0.03778212
### Test set diff
diff_drybean_tda_pca_5.60.5_lr.n3_test<-(db_lr_cf_ov_acc - db_tda_pc_5.60.5_n3_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_lr.n3_test
## Accuracy
## 0.3139706
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n3_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n3_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n3_test$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n3_test$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n3_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n3_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1603
##
## $winRight
## [1] 0.8397
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n3_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_lr.n3_test)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr.n3_test)) #bf_tda_pca_5.60.5_lr.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n3_test))
##Node4
DryBean_TDA_PC_5.60.5_n4_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_dry_bean_dataset_5.60.5.n4.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 72 (51 variable)
## initial value 827.617734
## iter 10 value 287.892769
## iter 20 value 48.290919
## iter 30 value 18.197327
## iter 40 value 16.385061
## iter 50 value 15.871089
## iter 60 value 15.366131
## iter 70 value 15.067464
## iter 80 value 14.879475
## iter 90 value 14.468115
## iter 100 value 13.967846
## final value 13.967846
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 827.617734
## iter 10 value 287.892858
## iter 20 value 56.437727
## iter 30 value 31.400890
## iter 40 value 22.781623
## iter 50 value 21.527134
## iter 60 value 21.301004
## iter 70 value 21.242925
## iter 80 value 21.235958
## iter 90 value 21.231339
## iter 100 value 21.222071
## final value 21.222071
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 827.617734
## iter 10 value 287.892769
## iter 20 value 48.301381
## iter 30 value 18.387086
## iter 40 value 16.725450
## iter 50 value 16.257114
## iter 60 value 15.777313
## iter 70 value 15.552494
## iter 80 value 15.377789
## iter 90 value 15.039381
## iter 100 value 14.817205
## final value 14.817205
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 824.845145
## iter 10 value 294.065897
## iter 20 value 80.703610
## iter 30 value 10.253629
## iter 40 value 6.693207
## iter 50 value 3.135012
## iter 60 value 0.040042
## final value 0.000081
## converged
## # weights: 72 (51 variable)
## initial value 824.845145
## iter 10 value 294.066025
## iter 20 value 92.825732
## iter 30 value 31.325549
## iter 40 value 19.300758
## iter 50 value 14.032298
## iter 60 value 12.390638
## iter 70 value 11.418843
## iter 80 value 10.247089
## iter 90 value 10.018751
## iter 100 value 9.905763
## final value 9.905763
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 824.845145
## iter 10 value 294.065897
## iter 20 value 80.722985
## iter 30 value 10.614155
## iter 40 value 7.811361
## iter 50 value 6.808006
## iter 60 value 6.449186
## iter 70 value 5.928460
## iter 80 value 5.804836
## iter 90 value 5.622462
## iter 100 value 5.560178
## final value 5.560178
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 826.231439
## iter 10 value 291.475138
## iter 20 value 56.914065
## iter 30 value 17.798466
## iter 40 value 15.810751
## iter 50 value 14.887052
## iter 60 value 13.662806
## iter 70 value 13.225798
## iter 80 value 12.573399
## iter 90 value 12.229413
## iter 100 value 11.980528
## final value 11.980528
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 826.231439
## iter 10 value 291.475356
## iter 20 value 73.251467
## iter 30 value 32.577708
## iter 40 value 23.331057
## iter 50 value 21.802098
## iter 60 value 21.507033
## iter 70 value 21.409488
## iter 80 value 21.397610
## iter 90 value 21.397044
## iter 100 value 21.396031
## final value 21.396031
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 826.231439
## iter 10 value 291.475138
## iter 20 value 56.934233
## iter 30 value 18.043020
## iter 40 value 16.305415
## iter 50 value 15.590873
## iter 60 value 14.773262
## iter 70 value 14.480837
## iter 80 value 14.048743
## iter 90 value 13.631088
## iter 100 value 13.054438
## final value 13.054438
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1239.347159
## iter 10 value 491.469166
## iter 20 value 144.769737
## iter 30 value 43.271148
## iter 40 value 35.006299
## iter 50 value 30.023970
## iter 60 value 29.283810
## iter 70 value 29.079232
## iter 80 value 28.987786
## iter 90 value 28.955490
## iter 100 value 28.928960
## final value 28.928960
## stopped after 100 iterations
DryBean_TDA_PC_5.60.5_n4_LrFit0
## Penalized Multinomial Regression
##
## 894 samples
## 16 predictor
## 4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 597, 595, 596
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9865771 0.9782208
## 1e-04 0.9854585 0.9763911
## 1e-01 0.9865883 0.9782445
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.1.
DryBean_TDA_PC_5.60.5_n4_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.9898990 0.9836367 Fold1
## 2 0.9899329 0.9837584 Fold3
## 3 0.9799331 0.9673385 Fold2
db_tda_pc_5.60.5_n4_lr_fit_re<-DryBean_TDA_PC_5.60.5_n4_LrFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n4_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 0.01498850 8.994831e-05 -0.17872410 0.09345407 0.25973206
## CALI -0.09819527 -7.546277e-04 -0.13674946 0.14665909 -0.06239474
## HOROZ 0.03738493 1.642183e-03 -0.04423673 0.83466570 0.98109527
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY -0.0002061687 0.00828700 0.0005875650 0.07700161 -0.01604308
## CALI -0.3144317239 -0.06668309 0.0005647395 0.38747829 1.78446833
## HOROZ 0.0654230588 0.01804208 -0.0024831873 -1.48717107 -1.57359246
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 0.01022276 0.006225626 0.01507791 0.0002847631 7.387258e-05
## CALI -0.07505404 -0.051267763 -0.06489145 -0.0020663072 -2.472824e-04
## HOROZ 0.02143842 0.031875922 0.02724669 0.0007367023 1.492520e-04
## ShapeFactor3 ShapeFactor4
## BOMBAY 0.01422970 0.01129805
## CALI -0.05400081 -0.09580005
## HOROZ 0.02231688 0.01887835
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 3.085765e-07 0.0041975019 0.0001299719 5.886092e-05 6.695408e-05
## CALI 6.753371e-06 0.0007049863 0.0052356293 2.140798e-03 6.322020e-04
## HOROZ 1.671316e-05 0.0008825559 0.0120565408 3.734688e-03 1.668966e-03
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 3.942434e-07 1.929994e-07 0.0041263391 6.798267e-05 2.436854e-07
## CALI 1.612936e-05 6.467529e-06 0.0007192361 1.118265e-03 4.179480e-06
## HOROZ 3.421790e-05 1.452213e-05 0.0009620845 2.469209e-03 9.954239e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 3.594836e-07 4.002417e-07 3.051704e-07 1.534208e-09 6.803567e-10
## CALI 6.600131e-06 4.390336e-06 4.364825e-06 6.014808e-08 8.807479e-09
## HOROZ 1.622201e-05 8.710748e-06 1.157344e-05 1.539993e-07 2.336175e-08
## ShapeFactor3 ShapeFactor4
## BOMBAY 2.952891e-07 3.482553e-07
## CALI 2.973951e-06 6.635355e-06
## HOROZ 8.020001e-06 1.633802e-05
##
## Residual Deviance: 57.85792
## AIC: 159.8579
vip(DryBean_TDA_PC_5.60.5_n4_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.60.5_n4_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_PC_5.60.5_n4_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n4_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n4_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n4_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 201 0 1 0 1 9 0
## BOMBAY 0 156 0 0 0 0 0
## CALI 180 0 479 53 13 226 437
## DERMASON 0 0 0 0 0 0 0
## HOROZ 15 0 9 1010 564 373 353
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3431
## 95% CI : (0.3286, 0.3579)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2467
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.50758 1.00000 0.9796 0.0000
## Specificity 0.99701 1.00000 0.7469 1.0000
## Pos Pred Value 0.94811 1.00000 0.3451 NaN
## Neg Pred Value 0.94959 1.00000 0.9963 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.04926 0.03824 0.1174 0.0000
## Detection Prevalence 0.05196 0.03824 0.3402 0.0000
## Balanced Accuracy 0.75229 1.00000 0.8632 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9758 0.000 0.0000
## Specificity 0.4974 1.000 1.0000
## Pos Pred Value 0.2427 NaN NaN
## Neg Pred Value 0.9920 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1382 0.000 0.0000
## Detection Prevalence 0.5696 0.000 0.0000
## Balanced Accuracy 0.7366 0.500 0.5000
db_tda_pc_5.60.5_n4_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 201 0 1 0 1 9 0
## BOMBAY 0 156 0 0 0 0 0
## CALI 180 0 479 53 13 226 437
## DERMASON 0 0 0 0 0 0 0
## HOROZ 15 0 9 1010 564 373 353
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3431
## 95% CI : (0.3286, 0.3579)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2467
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.50758 1.00000 0.9796 0.0000
## Specificity 0.99701 1.00000 0.7469 1.0000
## Pos Pred Value 0.94811 1.00000 0.3451 NaN
## Neg Pred Value 0.94959 1.00000 0.9963 0.7395
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.04926 0.03824 0.1174 0.0000
## Detection Prevalence 0.05196 0.03824 0.3402 0.0000
## Balanced Accuracy 0.75229 1.00000 0.8632 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9758 0.000 0.0000
## Specificity 0.4974 1.000 1.0000
## Pos Pred Value 0.2427 NaN NaN
## Neg Pred Value 0.9920 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1382 0.000 0.0000
## Detection Prevalence 0.5696 0.000 0.0000
## Balanced Accuracy 0.7366 0.500 0.5000
db_tda_pc_5.60.5_n4_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.431373e-01 2.467402e-01 3.285637e-01 3.579358e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 1.075906e-31 NaN
db_tda_pc_5.60.5_n4_db_lr_cf0_ov_acc<-db_tda_pc_5.60.5_n4_db_lr_cf0$overall[1]
db_tda_pc_5.60.5_n4_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.5075758 0.9970141 0.9481132 0.9495863 0.9481132
## Class: BOMBAY 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## Class: CALI 0.9795501 0.7468672 0.3451009 0.9962853 0.3451009
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.9757785 0.4974300 0.2426850 0.9920273 0.2426850
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.5075758 0.6611842 0.09705882 0.04926471
## Class: BOMBAY 1.0000000 1.0000000 0.03823529 0.03823529
## Class: CALI 0.9795501 0.5103889 0.11985294 0.11740196
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.9757785 0.3886975 0.14166667 0.13823529
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.05196078 0.7522949
## Class: BOMBAY 0.03823529 1.0000000
## Class: CALI 0.34019608 0.8632086
## Class: DERMASON 0.00000000 0.5000000
## Class: HOROZ 0.56960784 0.7366043
## Class: SEKER 0.00000000 0.5000000
## Class: SIRA 0.00000000 0.5000000
db_tda_pc_5.60.5_n4_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n4_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_lr_n4_3_fold<-(db_lr_fit_re - db_tda_pc_5.60.5_n4_lr_fit_re)
diff_drybean_tda_pca_5.60.5_lr_n4_3_fold
## Accuracy
## 1 -0.05885365
## 2 -0.06579061
## 3 -0.05639629
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n4_3_fold
## $winLeft
## [1] 0.9913
##
## $winRope
## [1] 0.0087
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n4_3_fold
## $left
## [1] 0.997932
##
## $rope
## [1] 0.001005499
##
## $right
## [1] 0.001062457
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold))
#bf_tda_pca_5.60.5_lr.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_lr_n4_3_fold)
## t = -21.454, df = 2, p-value = 0.002165
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.07244936 -0.04824435
## sample estimates:
## mean of x
## -0.06034685
### Test set diff
diff_drybean_tda_pca_5.60.5_lr.n4_test<-(db_lr_cf_ov_acc - db_tda_pc_5.60.5_n4_db_lr_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_lr.n4_test
## Accuracy
## 0.5813725
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_lr.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_lr.n4_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n4_test$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n4_test$probRight
bst_dbf_db_tda_pca_5.60.5_lr.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_lr.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_lr.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1598333
##
## $winRight
## [1] 0.8401667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_lr.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_lr.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_lr.n4_test)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr.n4_test)) #bf_tda_pca_5.60.5_lr.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n4_test))
##Node5
#DryBean_TDA_PC_5.60.5_n5_LrFit0 <- train(as.factor(Class) ~ .,
# data = tda.m_dry_bean_dataset_5.60.5.n5.vec,
# family = 'binomial',
# method = 'multinom',
# trControl = fitControl,
# metric='Accuracy')
#DryBean_TDA_PC_5.60.5_n5_LrFit0
#DryBean_TDA_PC_5.60.5_n5_LrFit0$resample
#db_tda_pc_5.60.5_n5_lr_fit_re<-DryBean_TDA_PC_5.60.5_n5_LrFit0$resample[1]
#summary(DryBean_TDA_PC_5.60.5_n5_LrFit0)
#vip(DryBean_TDA_PC_5.60.5_n5_LrFit0,50) + ggtitle("dryBean_TDA_PCA_5.60.5_n5_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_PC_5.60.5_n5_LrFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.60.5_n5_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.60.5_n5_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.60.5_n5_db_lr_cf0
#db_tda_pc_5.60.5_n5_db_lr_cf0
#db_tda_pc_5.60.5_n5_db_lr_cf0$overall
#db_tda_pc_5.60.5_n5_db_lr_cf0_ov_acc<-db_tda_pc_5.60.5_n5_db_lr_cf0$overall[1]
#db_tda_pc_5.60.5_n5_db_lr_cf0$byClass
#db_tda_pc_5.60.5_n5_db_lr_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n5_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted LR vs. tda-assisted LR classifiers
### 3-fold diff
#diff_drybean_tda_pca_5.60.5_lr_n5_3_fold<-(db_lr_fit_re - db_tda_pc_5.60.5_n5_lr_fit_re)
#diff_drybean_tda_pca_5.60.5_lr_n5_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_lr.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_lr.n5_3_fold
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_lr.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n5_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.60.5_lr.n5_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_lr.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n5_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_lr.n5_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_lr.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_lr.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_lr_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr_n5_3_fold))
#bf_tda_pca_5.60.5_lr.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr_n5_3_fold))
### Test set diff
#diff_drybean_tda_pca_5.60.5_lr.n5_test<-(db_lr_cf_ov_acc - db_tda_pc_5.60.5_n5_db_lr_cf0_ov_acc)
#diff_drybean_tda_pca_5.60.5_lr.n5_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_lr.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_lr.n5_test
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_lr.n5_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_lr.n5_test$probLeft/bst_dbf_db_tda_pca_5.60.5_lr.n5_test$probRight
#bst_dbf_db_tda_pca_5.60.5_lr.n5_test_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_lr.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_lr.n5_test
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_lr.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_lr.n5_test
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_lr.n5_test)))
#BayesFactor
#bf_tda_pca_5.60.5_lr.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_lr.n5_test)) #bf_tda_pca_5.60.5_lr.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_lr.n5_test))
##With TDA KDE filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_KDE_5.60.5_n1_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_kde_dry_bean_dataset_5.60.5.n1.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 6059.483264
## iter 20 value 4530.201197
## iter 30 value 3972.493806
## iter 40 value 1974.378425
## iter 50 value 835.438825
## iter 60 value 756.577437
## iter 70 value 739.206804
## iter 80 value 725.655896
## iter 90 value 716.340733
## iter 100 value 709.746780
## final value 709.746780
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 6059.483313
## iter 20 value 4530.202241
## iter 30 value 3972.546465
## iter 40 value 1928.535288
## iter 50 value 1025.404161
## iter 60 value 966.795741
## iter 70 value 923.974479
## iter 80 value 895.106905
## iter 90 value 878.168028
## iter 100 value 865.002917
## final value 865.002917
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 6059.483264
## iter 20 value 4530.201205
## iter 30 value 3972.494507
## iter 40 value 1974.450152
## iter 50 value 836.747058
## iter 60 value 758.354311
## iter 70 value 741.934193
## iter 80 value 729.944842
## iter 90 value 722.568423
## iter 100 value 717.853168
## final value 717.853168
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 5879.216581
## iter 20 value 4904.026065
## iter 30 value 4030.988425
## iter 40 value 1390.431316
## iter 50 value 770.859208
## iter 60 value 718.116738
## iter 70 value 693.418416
## iter 80 value 679.833150
## iter 90 value 674.339761
## iter 100 value 667.124353
## final value 667.124353
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 5879.216748
## iter 20 value 4904.032310
## iter 30 value 4031.414250
## iter 40 value 1769.667767
## iter 50 value 1002.017115
## iter 60 value 927.307954
## iter 70 value 862.611326
## iter 80 value 817.705986
## iter 90 value 798.369739
## iter 100 value 791.695521
## final value 791.695521
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 5879.216581
## iter 20 value 4904.026068
## iter 30 value 4030.988619
## iter 40 value 1390.644191
## iter 50 value 771.864692
## iter 60 value 720.421643
## iter 70 value 697.797280
## iter 80 value 686.514702
## iter 90 value 682.052319
## iter 100 value 676.654945
## final value 676.654945
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 6178.246134
## iter 20 value 4862.842258
## iter 30 value 3918.569711
## iter 40 value 1661.855366
## iter 50 value 776.113430
## iter 60 value 712.194885
## iter 70 value 697.007215
## iter 80 value 689.016624
## iter 90 value 682.073465
## iter 100 value 676.214911
## final value 676.214911
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 6178.246215
## iter 20 value 4862.843659
## iter 30 value 3918.650303
## iter 40 value 1748.092048
## iter 50 value 989.174997
## iter 60 value 885.228585
## iter 70 value 827.127153
## iter 80 value 790.753361
## iter 90 value 769.269261
## iter 100 value 764.581721
## final value 764.581721
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 9733.442566
## iter 10 value 6178.246134
## iter 20 value 4862.842256
## iter 30 value 3918.570134
## iter 40 value 1661.963094
## iter 50 value 775.817583
## iter 60 value 714.043441
## iter 70 value 699.590444
## iter 80 value 692.710767
## iter 90 value 687.284535
## iter 100 value 683.206683
## final value 683.206683
## stopped after 100 iterations
## # weights: 126 (102 variable)
## initial value 14600.163848
## iter 10 value 10284.583563
## iter 20 value 6872.715309
## iter 30 value 5119.574609
## iter 40 value 2297.884813
## iter 50 value 1195.667653
## iter 60 value 1114.272251
## iter 70 value 1088.761476
## iter 80 value 1073.215807
## iter 90 value 1063.458642
## iter 100 value 1055.748911
## final value 1055.748911
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n1_LrFit0
## Penalized Multinomial Regression
##
## 7503 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5002, 5002, 5002
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9488205 0.9383691
## 1e-04 0.9490870 0.9386901
## 1e-01 0.9462882 0.9353094
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.60.5_n1_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.9484206 0.9379118 Fold2
## 2 0.9516194 0.9417311 Fold1
## 3 0.9472211 0.9364273 Fold3
nb_tda_kde_5.60.5_n1_lr_fit_re<-DryBean_TDA_KDE_5.60.5_n1_LrFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n1_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 14.1873020 0.001910137 -0.12047989 0.5576488 1.1696856
## CALI 33.5184168 0.003184866 -0.17962449 2.0199688 2.5341731
## DERMASON 38.0431048 0.009141873 0.09035986 1.2498796 1.6271752
## HOROZ 2.7468507 0.007548058 0.08158868 2.1359222 4.1054014
## SEKER -0.6193353 0.003470135 0.16748254 0.8200154 0.2859032
## SIRA 40.2543945 0.004182689 -0.11497466 2.1790815 2.2248075
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 24.667244 16.037489 0.0003234268 -2.221372 4.0030903
## CALI -76.865527 107.587465 -0.0035012331 -3.779312 4.4126345
## DERMASON -24.713151 78.391220 -0.0067340966 -4.440149 -9.0606961
## HOROZ 2.685957 95.236092 -0.0067805832 -6.670236 -3.8510845
## SEKER -51.364667 7.829363 -0.0030895128 -2.078741 -0.1662761
## SIRA -90.257961 110.918979 -0.0047210431 -4.142088 -7.5258634
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2 ShapeFactor3
## BOMBAY 14.60846 14.96037 10.46364 0.2626964 0.04382577 7.246138
## CALI 41.21492 -49.64628 1.80558 0.7959565 0.03761688 -35.036270
## DERMASON 36.59657 72.14393 27.16034 0.8139716 0.27362270 7.156249
## HOROZ 29.45577 60.29710 -36.02820 0.9069928 -0.35315550 -74.244914
## SEKER -21.01748 113.64586 23.86429 -0.8450683 0.04221629 41.893587
## SIRA 31.67910 -15.50600 30.73015 -0.7853421 -0.13548583 2.755482
## ShapeFactor4
## BOMBAY 14.397608
## CALI -4.494654
## DERMASON 21.265123
## HOROZ -11.327108
## SEKER -1.228420
## SIRA 14.318826
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## BOMBAY 2.554177e-10 2.475484e-05 2.575865e-06 3.503085e-06 2.193147e-06
## CALI 9.443241e-06 2.917189e-04 1.068770e-03 3.082034e-03 3.883226e-03
## DERMASON 1.511044e-05 1.455946e-03 4.704652e-03 1.175883e-03 2.073625e-03
## HOROZ 3.386752e-06 3.762454e-04 1.950812e-03 1.094809e-03 9.967108e-04
## SEKER 4.887807e-06 7.018569e-04 2.217076e-03 4.970320e-04 6.749213e-04
## SIRA 6.609150e-06 4.729673e-04 2.252148e-03 2.444191e-03 2.408816e-03
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## BOMBAY 2.207759e-08 8.566822e-09 2.493829e-05 8.194967e-08 2.443112e-09
## CALI 2.786441e-05 4.237162e-06 2.891784e-04 1.364790e-03 9.029878e-06
## DERMASON 1.390394e-05 6.118372e-06 1.502147e-03 1.632351e-03 1.246220e-05
## HOROZ 1.211699e-05 3.196096e-06 3.749662e-04 5.266907e-04 2.862897e-06
## SEKER 4.647241e-06 2.592899e-06 7.067817e-04 6.172176e-04 3.857790e-06
## SIRA 2.435897e-05 6.901058e-06 4.780864e-04 8.176692e-04 7.087976e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## BOMBAY 5.199186e-10 3.539971e-09 5.741402e-09 3.028653e-11 2.338977e-11
## CALI 9.310259e-06 1.547656e-05 1.685731e-05 1.907626e-08 6.419389e-08
## DERMASON 1.497295e-05 1.532696e-05 1.572940e-05 1.386445e-07 7.296454e-08
## HOROZ 3.326442e-06 3.931668e-06 4.097965e-06 3.840487e-08 1.301324e-08
## SEKER 4.837576e-06 5.025636e-06 4.863660e-06 4.422700e-08 2.507452e-08
## SIRA 6.503149e-06 9.880136e-06 1.186400e-05 4.565126e-08 5.670666e-08
## ShapeFactor3 ShapeFactor4
## BOMBAY 9.377405e-09 2.278480e-10
## CALI 2.037878e-05 9.279112e-06
## DERMASON 1.592572e-05 1.509422e-05
## HOROZ 4.708549e-06 3.368891e-06
## SEKER 4.802159e-06 4.891802e-06
## SIRA 1.509138e-05 6.618731e-06
##
## Residual Deviance: 2111.498
## AIC: 2315.498
vip(DryBean_TDA_KDE_5.60.5_n1_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n1_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n1_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n1_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n1_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
nb_tda_kde_5.60.5_n1_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 352 0 14 0 1 7 2
## BOMBAY 0 156 0 0 0 0 0
## CALI 28 0 459 0 9 0 1
## DERMASON 0 0 0 825 2 6 18
## HOROZ 2 0 9 2 556 1 8
## SEKER 3 0 1 13 0 560 10
## SIRA 11 0 6 223 10 34 751
##
## Overall Statistics
##
## Accuracy : 0.8968
## 95% CI : (0.8871, 0.906)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8757
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.88889 1.00000 0.9387 0.7761
## Specificity 0.99349 1.00000 0.9894 0.9914
## Pos Pred Value 0.93617 1.00000 0.9235 0.9694
## Neg Pred Value 0.98812 1.00000 0.9916 0.9263
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08627 0.03824 0.1125 0.2022
## Detection Prevalence 0.09216 0.03824 0.1218 0.2086
## Balanced Accuracy 0.94119 1.00000 0.9640 0.8837
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9619 0.9211 0.9506
## Specificity 0.9937 0.9922 0.9137
## Pos Pred Value 0.9619 0.9540 0.7256
## Neg Pred Value 0.9937 0.9863 0.9872
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1363 0.1373 0.1841
## Detection Prevalence 0.1417 0.1439 0.2537
## Balanced Accuracy 0.9778 0.9566 0.9322
nb_tda_kde_5.60.5_n1_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 352 0 14 0 1 7 2
## BOMBAY 0 156 0 0 0 0 0
## CALI 28 0 459 0 9 0 1
## DERMASON 0 0 0 825 2 6 18
## HOROZ 2 0 9 2 556 1 8
## SEKER 3 0 1 13 0 560 10
## SIRA 11 0 6 223 10 34 751
##
## Overall Statistics
##
## Accuracy : 0.8968
## 95% CI : (0.8871, 0.906)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8757
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.88889 1.00000 0.9387 0.7761
## Specificity 0.99349 1.00000 0.9894 0.9914
## Pos Pred Value 0.93617 1.00000 0.9235 0.9694
## Neg Pred Value 0.98812 1.00000 0.9916 0.9263
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08627 0.03824 0.1125 0.2022
## Detection Prevalence 0.09216 0.03824 0.1218 0.2086
## Balanced Accuracy 0.94119 1.00000 0.9640 0.8837
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9619 0.9211 0.9506
## Specificity 0.9937 0.9922 0.9137
## Pos Pred Value 0.9619 0.9540 0.7256
## Neg Pred Value 0.9937 0.9863 0.9872
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1363 0.1373 0.1841
## Detection Prevalence 0.1417 0.1439 0.2537
## Balanced Accuracy 0.9778 0.9566 0.9322
nb_tda_kde_5.60.5_n1_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8968137 0.8756826 0.8870710 0.9059839 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n1_db_lr_cf0_ov_acc<-nb_tda_kde_5.60.5_n1_db_lr_cf0$overall[1]
nb_tda_kde_5.60.5_n1_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8888889 0.9934853 0.9361702 0.9881210 0.9361702
## Class: BOMBAY 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
## Class: CALI 0.9386503 0.9894180 0.9235412 0.9916271 0.9235412
## Class: DERMASON 0.7761054 0.9913822 0.9694477 0.9262930 0.9694477
## Class: HOROZ 0.9619377 0.9937179 0.9619377 0.9937179 0.9619377
## Class: SEKER 0.9210526 0.9922235 0.9540034 0.9862582 0.9540034
## Class: SIRA 0.9506329 0.9136778 0.7256039 0.9871921 0.7256039
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8888889 0.9119171 0.09705882 0.08627451
## Class: BOMBAY 1.0000000 1.0000000 0.03823529 0.03823529
## Class: CALI 0.9386503 0.9310345 0.11985294 0.11250000
## Class: DERMASON 0.7761054 0.8620690 0.26053922 0.20220588
## Class: HOROZ 0.9619377 0.9619377 0.14166667 0.13627451
## Class: SEKER 0.9210526 0.9372385 0.14901961 0.13725490
## Class: SIRA 0.9506329 0.8230137 0.19362745 0.18406863
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09215686 0.9411871
## Class: BOMBAY 0.03823529 1.0000000
## Class: CALI 0.12181373 0.9640341
## Class: DERMASON 0.20857843 0.8837438
## Class: HOROZ 0.14166667 0.9778278
## Class: SEKER 0.14387255 0.9566381
## Class: SIRA 0.25367647 0.9321554
nb_tda_kde_5.60.5_n1_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n1_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_lr_n1_3_fold<-(db_lr_fit_re - nb_tda_kde_5.60.5_n1_lr_fit_re)
diff_drybean_tda_kde_5.60.5_lr_n1_3_fold
## Accuracy
## 1 -0.01737529
## 2 -0.02747708
## 3 -0.02368430
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n1_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n1_3_fold_odds.left<-bst_tda_kde_5.60.5_lr.n1_3_fold$probLeft/bst_tda_kde_5.60.5_lr.n1_3_fold$probRight
bst_tda_kde_5.60.5_lr.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n1_3_fold
## $winLeft
## [1] 0.9620333
##
## $winRope
## [1] 0.03796667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n1_3_fold
## $left
## [1] 0.9682372
##
## $rope
## [1] 0.02648391
##
## $right
## [1] 0.005278865
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold))
#bf_tda_kde_5.60.5_lr.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_lr_n1_3_fold)
## t = -7.7544, df = 2, p-value = 0.01623
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.03552175 -0.01016936
## sample estimates:
## mean of x
## -0.02284556
### Test set diff
diff_drybean_tda_kde_5.60.5_lr.n1_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n1_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_lr.n1_test
## Accuracy
## 0.03063725
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n1_test),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n1_test_odds.left<-bst_tda_kde_5.60.5_lr.n1_test$probLeft/bst_tda_kde_5.60.5_lr.n1_test$probRight
bst_tda_kde_5.60.5_lr.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n1_test),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1599
##
## $winRight
## [1] 0.8401
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_lr.n1_test)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr.n1_test)) #bf_tda_pca_5.60.5_lr.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n1_test))
##With TDA KDE filter 5 intervals, 50% overlap, 5 bins
##Node2
DryBean_TDA_KDE_5.60.5_n2_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_kde_dry_bean_dataset_5.60.5.n2.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 108 (85 variable)
## initial value 8363.933202
## iter 10 value 4529.405865
## iter 20 value 2975.194461
## iter 30 value 1760.433512
## iter 40 value 793.794227
## iter 50 value 763.471977
## iter 60 value 745.234855
## iter 70 value 739.382643
## iter 80 value 732.993540
## iter 90 value 728.217000
## iter 100 value 722.392251
## final value 722.392251
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8363.933202
## iter 10 value 4529.406595
## iter 20 value 2975.201500
## iter 30 value 1781.819801
## iter 40 value 910.404722
## iter 50 value 835.909962
## iter 60 value 816.366257
## iter 70 value 809.202983
## iter 80 value 807.716542
## iter 90 value 807.097612
## iter 100 value 806.883085
## final value 806.883085
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8363.933202
## iter 10 value 4529.405865
## iter 20 value 2975.194369
## iter 30 value 1760.392279
## iter 40 value 794.275196
## iter 50 value 764.476650
## iter 60 value 747.322616
## iter 70 value 742.090976
## iter 80 value 736.593332
## iter 90 value 732.779834
## iter 100 value 728.570481
## final value 728.570481
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8365.724962
## iter 10 value 4668.934489
## iter 20 value 3011.908700
## iter 30 value 2055.850719
## iter 40 value 753.397906
## iter 50 value 713.344949
## iter 60 value 696.277845
## iter 70 value 685.083487
## iter 80 value 677.606203
## iter 90 value 673.303612
## iter 100 value 670.969909
## final value 670.969909
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8365.724962
## iter 10 value 4668.935267
## iter 20 value 3011.916016
## iter 30 value 2063.154118
## iter 40 value 869.744551
## iter 50 value 771.494679
## iter 60 value 756.950826
## iter 70 value 749.350767
## iter 80 value 746.248577
## iter 90 value 745.097985
## iter 100 value 744.987860
## final value 744.987860
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8365.724962
## iter 10 value 4668.934491
## iter 20 value 3011.908728
## iter 30 value 2055.869811
## iter 40 value 753.869284
## iter 50 value 714.486400
## iter 60 value 698.550409
## iter 70 value 688.821008
## iter 80 value 682.561772
## iter 90 value 679.283684
## iter 100 value 677.572120
## final value 677.572120
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8362.141443
## iter 10 value 5188.503036
## iter 20 value 3702.806714
## iter 30 value 2340.505925
## iter 40 value 850.301759
## iter 50 value 783.691915
## iter 60 value 764.838663
## iter 70 value 753.717350
## iter 80 value 745.419695
## iter 90 value 739.198003
## iter 100 value 735.698477
## final value 735.698477
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8362.141443
## iter 10 value 5188.503244
## iter 20 value 3702.817208
## iter 30 value 2577.547595
## iter 40 value 993.970023
## iter 50 value 861.361209
## iter 60 value 842.043854
## iter 70 value 831.511415
## iter 80 value 828.470743
## iter 90 value 827.677085
## iter 100 value 827.413071
## final value 827.413071
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 8362.141443
## iter 10 value 5188.503036
## iter 20 value 3702.806718
## iter 30 value 2340.762914
## iter 40 value 850.715824
## iter 50 value 784.704543
## iter 60 value 766.799887
## iter 70 value 756.677450
## iter 80 value 749.533503
## iter 90 value 744.672385
## iter 100 value 742.208347
## final value 742.208347
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 12545.899804
## iter 10 value 7697.972917
## iter 20 value 5416.041157
## iter 30 value 3014.404294
## iter 40 value 1186.881966
## iter 50 value 1136.218786
## iter 60 value 1109.476463
## iter 70 value 1101.067754
## iter 80 value 1094.292476
## iter 90 value 1084.657983
## iter 100 value 1081.524740
## final value 1081.524740
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n2_LrFit0
## Penalized Multinomial Regression
##
## 7002 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4668, 4669, 4667
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9487283 0.9340500
## 1e-04 0.9492996 0.9347812
## 1e-01 0.9453007 0.9296161
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.60.5_n2_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.9459923 0.9305013 Fold2
## 2 0.9520137 0.9382914 Fold1
## 3 0.9498929 0.9355508 Fold3
nb_tda_kde_5.60.5_n2_lr_fit_re<-DryBean_TDA_KDE_5.60.5_n2_LrFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n2_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 14.258370 0.002786820 -0.165385866 2.3087941 2.7271582
## DERMASON 23.665719 0.001510649 -0.014862770 1.5566624 1.0250175
## HOROZ -11.326835 0.008371033 -0.008443637 2.1046390 4.6884226
## SEKER -9.714985 0.003289476 0.072735728 -0.1173257 -0.3014453
## SIRA 60.936084 0.003769916 -0.400787685 2.2712774 2.7064724
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI -45.94758 12.12644 -0.003111714 -4.3420987 3.530103
## DERMASON -66.71197 36.70367 -0.003553877 -2.4079705 -18.290885
## HOROZ 62.32881 52.86067 -0.006832751 -7.0692336 -6.712081
## SEKER 27.89356 -65.63539 -0.002755505 -0.1773836 -13.543652
## SIRA -59.35067 102.97293 -0.004680084 -3.4967537 -10.810594
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 18.51498 -28.36063 13.441361 0.24706329 0.15448412
## DERMASON 12.47553 48.53314 21.553689 0.04367718 0.07710992
## HOROZ 27.41676 24.89534 -35.768760 0.14648568 -0.36569180
## SEKER -23.42929 78.75839 8.971654 -0.18812953 0.11987296
## SIRA 34.19692 -143.16977 42.156966 0.98301266 0.32362909
## ShapeFactor3 ShapeFactor4
## CALI 9.883379 5.943753
## DERMASON 7.673164 8.978022
## HOROZ -52.446071 -6.645987
## SEKER 34.326637 7.342270
## SIRA 7.993418 21.954292
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 4.613621e-06 0.0005644030 0.001926715 0.0009832870 0.0008651138
## DERMASON 9.920513e-06 0.0010607738 0.003393116 0.0018978598 0.0020829211
## HOROZ 4.653529e-06 0.0006802816 0.002082293 0.0008633007 0.0007582899
## SEKER 8.533448e-06 0.0012122352 0.003264158 0.0006686185 0.0014988936
## SIRA 8.794368e-06 0.0007047805 0.002665055 0.0036888613 0.0037452725
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI 8.952414e-06 3.375103e-06 0.0005620807 0.0006341793 3.711622e-06
## DERMASON 2.152876e-05 8.089544e-06 0.0010782060 0.0010730763 9.371575e-06
## HOROZ 7.442639e-06 3.240741e-06 0.0006770147 0.0006821488 4.492655e-06
## SEKER 6.835039e-06 3.129554e-06 0.0012179411 0.0010586643 7.487998e-06
## SIRA 3.922985e-05 1.206936e-05 0.0007096906 0.0010760031 1.042811e-05
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 4.553898e-06 4.996499e-06 4.854145e-06 3.684644e-08 1.795396e-08
## DERMASON 9.797809e-06 1.039456e-05 1.308737e-05 8.973257e-08 6.855666e-08
## HOROZ 4.571632e-06 4.636117e-06 4.466641e-06 3.681869e-08 1.511064e-08
## SEKER 8.446777e-06 8.263454e-06 9.465061e-06 6.438619e-08 3.903970e-08
## SIRA 8.665648e-06 1.461838e-05 1.893891e-05 5.306657e-08 1.003184e-07
## ShapeFactor3 ShapeFactor4
## CALI 5.112119e-06 4.578253e-06
## DERMASON 1.552199e-05 9.916891e-06
## HOROZ 4.289408e-06 4.590739e-06
## SEKER 9.914373e-06 8.536828e-06
## SIRA 2.502352e-05 8.812527e-06
##
## Residual Deviance: 2163.049
## AIC: 2333.049
vip(DryBean_TDA_KDE_5.60.5_n2_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n2_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n2_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n2_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n2_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n2_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 344 0 13 1 1 7 5
## BOMBAY 0 0 0 0 0 0 0
## CALI 26 0 462 0 19 0 1
## DERMASON 0 0 0 960 3 7 63
## HOROZ 8 156 6 9 545 1 8
## SEKER 3 0 1 14 0 569 8
## SIRA 15 0 7 79 10 24 705
##
## Overall Statistics
##
## Accuracy : 0.8787
## 95% CI : (0.8683, 0.8885)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8526
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.86869 0.00000 0.9448 0.9031
## Specificity 0.99267 1.00000 0.9872 0.9758
## Pos Pred Value 0.92722 NaN 0.9094 0.9293
## Neg Pred Value 0.98598 0.96176 0.9924 0.9662
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08431 0.00000 0.1132 0.2353
## Detection Prevalence 0.09093 0.00000 0.1245 0.2532
## Balanced Accuracy 0.93068 0.50000 0.9660 0.9395
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9429 0.9359 0.8924
## Specificity 0.9463 0.9925 0.9590
## Pos Pred Value 0.7435 0.9563 0.8393
## Neg Pred Value 0.9901 0.9888 0.9738
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1336 0.1395 0.1728
## Detection Prevalence 0.1797 0.1458 0.2059
## Balanced Accuracy 0.9446 0.9642 0.9257
nb_tda_kde_5.60.5_n2_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 344 0 13 1 1 7 5
## BOMBAY 0 0 0 0 0 0 0
## CALI 26 0 462 0 19 0 1
## DERMASON 0 0 0 960 3 7 63
## HOROZ 8 156 6 9 545 1 8
## SEKER 3 0 1 14 0 569 8
## SIRA 15 0 7 79 10 24 705
##
## Overall Statistics
##
## Accuracy : 0.8787
## 95% CI : (0.8683, 0.8885)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8526
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.86869 0.00000 0.9448 0.9031
## Specificity 0.99267 1.00000 0.9872 0.9758
## Pos Pred Value 0.92722 NaN 0.9094 0.9293
## Neg Pred Value 0.98598 0.96176 0.9924 0.9662
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.08431 0.00000 0.1132 0.2353
## Detection Prevalence 0.09093 0.00000 0.1245 0.2532
## Balanced Accuracy 0.93068 0.50000 0.9660 0.9395
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9429 0.9359 0.8924
## Specificity 0.9463 0.9925 0.9590
## Pos Pred Value 0.7435 0.9563 0.8393
## Neg Pred Value 0.9901 0.9888 0.9738
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1336 0.1395 0.1728
## Detection Prevalence 0.1797 0.1458 0.2059
## Balanced Accuracy 0.9446 0.9642 0.9257
nb_tda_kde_5.60.5_n2_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8786765 0.8526265 0.8682634 0.8885438 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n2_db_lr_cf0_ov_acc<-nb_tda_kde_5.60.5_n2_db_lr_cf0$overall[1]
nb_tda_kde_5.60.5_n2_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8686869 0.9926710 0.9272237 0.9859800 0.9272237
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.9447853 0.9871902 0.9094488 0.9924412 0.9094488
## Class: DERMASON 0.9031044 0.9758038 0.9293320 0.9661963 0.9293320
## Class: HOROZ 0.9429066 0.9463164 0.7435198 0.9901404 0.7435198
## Class: SEKER 0.9358553 0.9925115 0.9563025 0.9888092 0.9563025
## Class: SIRA 0.8924051 0.9589666 0.8392857 0.9737654 0.8392857
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8686869 0.8970013 0.09705882 0.08431373
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.9447853 0.9267803 0.11985294 0.11323529
## Class: DERMASON 0.9031044 0.9160305 0.26053922 0.23529412
## Class: HOROZ 0.9429066 0.8314264 0.14166667 0.13357843
## Class: SEKER 0.9358553 0.9459684 0.14901961 0.13946078
## Class: SIRA 0.8924051 0.8650307 0.19362745 0.17279412
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09093137 0.9306789
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.12450980 0.9659877
## Class: DERMASON 0.25318627 0.9394541
## Class: HOROZ 0.17965686 0.9446115
## Class: SEKER 0.14583333 0.9641834
## Class: SIRA 0.20588235 0.9256858
nb_tda_kde_5.60.5_n2_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n2_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_lr_n2_3_fold<-(db_lr_fit_re - nb_tda_kde_5.60.5_n2_lr_fit_re)
diff_drybean_tda_kde_5.60.5_lr_n2_3_fold
## Accuracy
## 1 -0.01494694
## 2 -0.02787144
## 3 -0.02635612
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n2_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n2_3_fold_odds.left<-bst_tda_kde_5.60.5_lr.n2_3_fold$probLeft/bst_tda_kde_5.60.5_lr.n2_3_fold$probRight
bst_tda_kde_5.60.5_lr.n2_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n2_3_fold
## $winLeft
## [1] 0.9648667
##
## $winRope
## [1] 0.03513333
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n2_3_fold
## $left
## [1] 0.9453966
##
## $rope
## [1] 0.04475193
##
## $right
## [1] 0.009851494
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold))
#bf_tda_kde_5.60.5_lr.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_lr_n2_3_fold)
## t = -5.6527, df = 2, p-value = 0.0299
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.040609262 -0.005507071
## sample estimates:
## mean of x
## -0.02305817
### Test set diff
diff_drybean_tda_kde_5.60.5_lr.n2_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n2_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_lr.n2_test
## Accuracy
## 0.04877451
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n2_test),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n2_test_odds.left<-bst_tda_kde_5.60.5_lr.n2_test$probLeft/bst_tda_kde_5.60.5_lr.n2_test$probRight
bst_tda_kde_5.60.5_lr.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n2_test),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1596333
##
## $winRight
## [1] 0.8403667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_lr.n2_test)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr.n2_test)) #bf_tda_pca_5.60.5_lr.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n2_test))
##Node3
DryBean_TDA_KDE_5.60.5_n3_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_kde_dry_bean_dataset_5.60.5.n3.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 108 (85 variable)
## initial value 4194.508917
## iter 10 value 1501.430592
## iter 20 value 1188.490036
## iter 30 value 741.090249
## iter 40 value 585.123317
## iter 50 value 563.112608
## iter 60 value 555.381005
## iter 70 value 547.163422
## iter 80 value 544.993611
## iter 90 value 543.285316
## iter 100 value 542.010568
## final value 542.010568
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 4194.508917
## iter 10 value 1501.431143
## iter 20 value 1188.495968
## iter 30 value 762.055029
## iter 40 value 606.685797
## iter 50 value 591.334111
## iter 60 value 590.277173
## iter 70 value 589.511194
## iter 80 value 589.457278
## iter 90 value 589.435230
## iter 100 value 589.433980
## final value 589.433980
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 4194.508917
## iter 10 value 1501.430592
## iter 20 value 1188.490032
## iter 30 value 741.147495
## iter 40 value 585.282752
## iter 50 value 563.816355
## iter 60 value 557.092187
## iter 70 value 550.595444
## iter 80 value 549.045571
## iter 90 value 547.859955
## iter 100 value 547.208330
## final value 547.208330
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 4196.300677
## iter 10 value 1466.957586
## iter 20 value 1165.680062
## iter 30 value 713.989079
## iter 40 value 574.017824
## iter 50 value 554.486127
## iter 60 value 547.044841
## iter 70 value 537.159120
## iter 80 value 535.330260
## iter 90 value 532.156078
## iter 100 value 531.065619
## final value 531.065619
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 4196.300677
## iter 10 value 1466.958126
## iter 20 value 1165.686842
## iter 30 value 724.548001
## iter 40 value 588.704164
## iter 50 value 578.871648
## iter 60 value 577.962916
## iter 70 value 577.308386
## iter 80 value 577.243138
## iter 90 value 577.204032
## iter 100 value 577.198629
## final value 577.198629
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 4196.300677
## iter 10 value 1466.957586
## iter 20 value 1165.680103
## iter 30 value 714.012458
## iter 40 value 574.176158
## iter 50 value 555.336695
## iter 60 value 548.858975
## iter 70 value 541.006354
## iter 80 value 539.689043
## iter 90 value 538.036618
## iter 100 value 537.357309
## final value 537.357309
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'CALI' is empty
## # weights: 90 (68 variable)
## initial value 3764.475277
## iter 10 value 1076.736535
## iter 20 value 978.645184
## iter 30 value 628.178382
## iter 40 value 599.952990
## iter 50 value 586.626278
## iter 60 value 577.400302
## iter 70 value 574.268150
## iter 80 value 568.324523
## iter 90 value 566.262803
## iter 100 value 564.852887
## final value 564.852887
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'CALI' is empty
## # weights: 90 (68 variable)
## initial value 3764.475277
## iter 10 value 1076.738104
## iter 20 value 978.710253
## iter 30 value 641.794946
## iter 40 value 623.822100
## iter 50 value 621.752188
## iter 60 value 621.367087
## iter 70 value 621.362652
## final value 621.362479
## converged
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'CALI' is empty
## # weights: 90 (68 variable)
## initial value 3764.475277
## iter 10 value 1076.736536
## iter 20 value 978.644837
## iter 30 value 628.222304
## iter 40 value 600.392431
## iter 50 value 588.159277
## iter 60 value 580.620072
## iter 70 value 578.270958
## iter 80 value 574.523290
## iter 90 value 573.490643
## iter 100 value 572.638542
## final value 572.638542
## stopped after 100 iterations
## # weights: 108 (85 variable)
## initial value 6290.867496
## iter 10 value 1864.536088
## iter 20 value 1214.473661
## iter 30 value 1007.066006
## iter 40 value 886.163908
## iter 50 value 863.447491
## iter 60 value 847.573104
## iter 70 value 844.321998
## iter 80 value 840.833495
## iter 90 value 839.184336
## iter 100 value 838.304856
## final value 838.304856
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n3_LrFit0
## Penalized Multinomial Regression
##
## 3511 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 2341, 2342, 2339
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.9119798 0.8667027
## 1e-04 0.9119801 0.8667075
## 1e-01 0.9077119 0.8602176
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.60.5_n3_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.8999145 0.8482197 Fold2
## 2 0.9111111 0.8654072 Fold1
## 3 0.9249147 0.8864955 Fold3
nb_tda_kde_5.60.5_n3_lr_fit_re<-DryBean_TDA_KDE_5.60.5_n2_LrFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n3_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 2.005974 -0.022194604 -0.40534324 -12.4609350 -19.195421
## DERMASON -21.736382 0.001486469 -0.08635313 1.3903655 2.155261
## HOROZ 6.116376 0.009766171 -0.05505338 2.7822719 4.468584
## SEKER -12.684383 0.013452529 -0.04898949 -0.3480099 -1.980234
## SIRA 29.219868 0.005288503 -0.18154725 2.3608652 3.619749
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI 4.278773 -1.818747 0.021840514 32.313579 -19.67185
## DERMASON 1.090549 9.835796 -0.007524805 -1.588432 -27.97048
## HOROZ 28.078349 22.029341 -0.010829198 -6.900758 -31.56502
## SEKER 3.948898 -154.771251 -0.016594774 3.111772 -29.18092
## SIRA -34.606400 111.142340 -0.006459143 -5.121273 -20.24508
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 1.970375 1.971100 2.421876 0.03574853 0.02164315
## DERMASON -33.710481 22.916955 -24.306509 -0.30904693 -0.23321027
## HOROZ 8.131654 10.996324 -2.632900 0.24734110 -0.09328766
## SEKER -15.497369 3.090331 29.723739 -0.54368815 0.49175547
## SIRA 39.215199 -45.439368 3.303731 0.57066829 -0.09243678
## ShapeFactor3 ShapeFactor4
## CALI 3.097229 2.951293
## DERMASON -26.776334 -21.128392
## HOROZ -9.424449 4.909219
## SEKER 78.360792 15.216318
## SIRA -31.117770 4.577506
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## CALI 3.037860e-08 0.0001426701 1.841498e-05 4.423057e-06 6.778082e-06
## DERMASON 9.056845e-06 0.0022504591 1.993228e-03 3.435719e-03 3.496784e-03
## HOROZ 9.990315e-06 0.0025429288 3.883587e-03 1.436251e-03 8.879165e-04
## SEKER 9.249525e-06 0.0026063592 3.091714e-03 7.712704e-04 1.199584e-03
## SIRA 9.657324e-06 0.0021767226 2.062003e-03 3.426201e-03 3.689496e-03
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## CALI 4.895298e-08 2.048300e-08 0.0001467784 3.780562e-06 2.269555e-08
## DERMASON 3.748287e-05 1.368599e-05 0.0022133288 9.637397e-04 9.684303e-06
## HOROZ 1.629773e-05 7.970140e-06 0.0024957374 1.129207e-03 7.449418e-06
## SEKER 8.223863e-06 4.271533e-06 0.0025756272 1.036926e-03 7.890147e-06
## SIRA 3.744933e-05 1.313394e-05 0.0021415093 1.059634e-03 1.071252e-05
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## CALI 3.282868e-08 6.669255e-08 3.917433e-08 2.320638e-10 1.807046e-10
## DERMASON 8.924768e-06 1.320300e-05 1.927380e-05 6.412941e-08 1.133702e-07
## HOROZ 9.821887e-06 8.480213e-06 7.767455e-06 1.088252e-07 2.448294e-08
## SEKER 9.172683e-06 9.522508e-06 9.219170e-06 8.243931e-08 3.965009e-08
## SIRA 9.538463e-06 1.403405e-05 2.030872e-05 5.599281e-08 1.166531e-07
## ShapeFactor3 ShapeFactor4
## CALI 4.548933e-08 3.071443e-08
## DERMASON 2.590742e-05 9.073007e-06
## HOROZ 6.027663e-06 9.922019e-06
## SEKER 8.962286e-06 9.244213e-06
## SIRA 2.698879e-05 9.698527e-06
##
## Residual Deviance: 1676.61
## AIC: 1846.61
vip(DryBean_TDA_KDE_5.60.5_n3_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n3_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n3_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n3_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n3_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n3_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 389 156 477 1 33 26 28
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 2 1
## DERMASON 0 0 0 974 3 10 80
## HOROZ 0 0 3 16 500 0 7
## SEKER 0 0 0 18 0 554 8
## SIRA 7 0 9 54 42 16 666
##
## Overall Statistics
##
## Accuracy : 0.7556
## 95% CI : (0.7421, 0.7688)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.705
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98232 0.00000 0.0000000 0.9163
## Specificity 0.80429 1.00000 0.9991646 0.9692
## Pos Pred Value 0.35045 NaN 0.0000000 0.9128
## Neg Pred Value 0.99764 0.96176 0.8800589 0.9705
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.09534 0.00000 0.0000000 0.2387
## Detection Prevalence 0.27206 0.00000 0.0007353 0.2615
## Balanced Accuracy 0.89331 0.50000 0.4995823 0.9427
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8651 0.9112 0.8430
## Specificity 0.9926 0.9925 0.9611
## Pos Pred Value 0.9506 0.9552 0.8388
## Neg Pred Value 0.9781 0.9846 0.9623
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1225 0.1358 0.1632
## Detection Prevalence 0.1289 0.1422 0.1946
## Balanced Accuracy 0.9288 0.9518 0.9021
nb_tda_kde_5.60.5_n3_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 389 156 477 1 33 26 28
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 2 1
## DERMASON 0 0 0 974 3 10 80
## HOROZ 0 0 3 16 500 0 7
## SEKER 0 0 0 18 0 554 8
## SIRA 7 0 9 54 42 16 666
##
## Overall Statistics
##
## Accuracy : 0.7556
## 95% CI : (0.7421, 0.7688)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.705
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.98232 0.00000 0.0000000 0.9163
## Specificity 0.80429 1.00000 0.9991646 0.9692
## Pos Pred Value 0.35045 NaN 0.0000000 0.9128
## Neg Pred Value 0.99764 0.96176 0.8800589 0.9705
## Prevalence 0.09706 0.03824 0.1198529 0.2605
## Detection Rate 0.09534 0.00000 0.0000000 0.2387
## Detection Prevalence 0.27206 0.00000 0.0007353 0.2615
## Balanced Accuracy 0.89331 0.50000 0.4995823 0.9427
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.8651 0.9112 0.8430
## Specificity 0.9926 0.9925 0.9611
## Pos Pred Value 0.9506 0.9552 0.8388
## Neg Pred Value 0.9781 0.9846 0.9623
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1225 0.1358 0.1632
## Detection Prevalence 0.1289 0.1422 0.1946
## Balanced Accuracy 0.9288 0.9518 0.9021
nb_tda_kde_5.60.5_n3_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.7556373 0.7049616 0.7421491 0.7687584 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n3_db_lr_cf0_ov_acc<-nb_tda_kde_5.60.5_n3_db_lr_cf0$overall[1]
nb_tda_kde_5.60.5_n3_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.9823232 0.8042888 0.3504505 0.9976431 0.3504505
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 0.9991646 0.0000000 0.8800589 0.0000000
## Class: DERMASON 0.9162747 0.9691747 0.9128397 0.9704613 0.9128397
## Class: HOROZ 0.8650519 0.9925757 0.9505703 0.9780529 0.9505703
## Class: SEKER 0.9111842 0.9925115 0.9551724 0.9845714 0.9551724
## Class: SIRA 0.8430380 0.9610942 0.8387909 0.9622642 0.8387909
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.9823232 0.5166003 0.09705882 0.09534314
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.0000000 NaN 0.11985294 0.00000000
## Class: DERMASON 0.9162747 0.9145540 0.26053922 0.23872549
## Class: HOROZ 0.8650519 0.9057971 0.14166667 0.12254902
## Class: SEKER 0.9111842 0.9326599 0.14901961 0.13578431
## Class: SIRA 0.8430380 0.8409091 0.19362745 0.16323529
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.2720588235 0.8933060
## Class: BOMBAY 0.0000000000 0.5000000
## Class: CALI 0.0007352941 0.4995823
## Class: DERMASON 0.2615196078 0.9427247
## Class: HOROZ 0.1289215686 0.9288138
## Class: SEKER 0.1421568627 0.9518479
## Class: SIRA 0.1946078431 0.9020661
nb_tda_kde_5.60.5_n3_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n3_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_lr_n3_3_fold<-(db_lr_fit_re - nb_tda_kde_5.60.5_n3_lr_fit_re)
diff_drybean_tda_kde_5.60.5_lr_n3_3_fold
## Accuracy
## 1 -0.01494694
## 2 -0.02787144
## 3 -0.02635612
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n3_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n3_3_fold_odds.left<-bst_tda_kde_5.60.5_lr.n3_3_fold$probLeft/bst_tda_kde_5.60.5_lr.n3_3_fold$probRight
bst_tda_kde_5.60.5_lr.n3_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n3_3_fold
## $winLeft
## [1] 0.9622333
##
## $winRope
## [1] 0.03776667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n3_3_fold
## $left
## [1] 0.9453966
##
## $rope
## [1] 0.04475193
##
## $right
## [1] 0.009851494
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold))
#bf_tda_kde_5.60.5_lr.n3_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_lr_n3_3_fold)
## t = -5.6527, df = 2, p-value = 0.0299
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.040609262 -0.005507071
## sample estimates:
## mean of x
## -0.02305817
### Test set diff
diff_drybean_tda_kde_5.60.5_lr.n3_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n3_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_lr.n3_test
## Accuracy
## 0.1718137
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n3_test),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n3_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n3_test_odds.left<-bst_tda_kde_5.60.5_lr.n3_test$probLeft/bst_tda_kde_5.60.5_lr.n3_test$probRight
bst_tda_kde_5.60.5_lr.n3_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n2_test),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1612
##
## $winRight
## [1] 0.8388
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n3_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n3_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_lr.n3_test)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr.n3_test)) #bf_tda_pca_5.60.5_lr.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n3_test))
##Node4
DryBean_TDA_KDE_5.60.5_n4_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_kde_dry_bean_dataset_5.60.5.n4.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## # weights: 72 (51 variable)
## initial value 1626.123286
## iter 10 value 720.671191
## iter 20 value 532.879933
## iter 30 value 474.949742
## iter 40 value 470.725407
## iter 50 value 468.283500
## iter 60 value 467.625199
## iter 70 value 466.959102
## iter 80 value 461.828096
## iter 90 value 459.929792
## iter 100 value 456.891479
## final value 456.891479
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1626.123286
## iter 10 value 720.684431
## iter 20 value 540.578907
## iter 30 value 487.429712
## iter 40 value 486.218798
## final value 486.087150
## converged
## # weights: 72 (51 variable)
## initial value 1626.123286
## iter 10 value 720.671204
## iter 20 value 532.889920
## iter 30 value 475.009897
## iter 40 value 471.074839
## iter 50 value 468.982566
## iter 60 value 468.494464
## iter 70 value 468.031536
## iter 80 value 467.138320
## iter 90 value 467.033190
## iter 100 value 466.960591
## final value 466.960591
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1624.736991
## iter 10 value 781.190509
## iter 20 value 541.075655
## iter 30 value 487.290991
## iter 40 value 483.777412
## iter 50 value 481.033469
## iter 60 value 479.484664
## iter 70 value 478.295501
## iter 80 value 476.037894
## iter 90 value 472.027620
## iter 100 value 470.980801
## final value 470.980801
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1624.736991
## iter 10 value 781.201726
## iter 20 value 545.404791
## iter 30 value 498.470858
## iter 40 value 497.660956
## iter 50 value 496.966569
## iter 50 value 496.966567
## iter 50 value 496.966567
## final value 496.966567
## converged
## # weights: 72 (51 variable)
## initial value 1624.736991
## iter 10 value 781.190520
## iter 20 value 541.079652
## iter 30 value 487.365386
## iter 40 value 484.061087
## iter 50 value 481.610828
## iter 60 value 480.358571
## iter 70 value 479.648317
## iter 80 value 478.691068
## iter 90 value 478.057179
## iter 100 value 477.943398
## final value 477.943398
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1626.123286
## iter 10 value 752.983341
## iter 20 value 566.626083
## iter 30 value 515.278704
## iter 40 value 509.707541
## iter 50 value 508.824548
## iter 60 value 507.274177
## iter 70 value 506.179782
## iter 80 value 502.930086
## iter 90 value 500.875425
## iter 100 value 499.585046
## final value 499.585046
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1626.123286
## iter 10 value 752.999584
## iter 20 value 568.559530
## iter 30 value 521.196806
## iter 40 value 517.343163
## final value 517.338341
## converged
## # weights: 72 (51 variable)
## initial value 1626.123286
## iter 10 value 752.983356
## iter 20 value 566.627544
## iter 30 value 515.314670
## iter 40 value 509.874066
## iter 50 value 509.085829
## iter 60 value 508.003916
## iter 70 value 507.415184
## iter 80 value 506.492400
## iter 90 value 506.235796
## iter 100 value 506.151775
## final value 506.151775
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 2438.491781
## iter 10 value 1080.028813
## iter 20 value 822.329903
## iter 30 value 765.256661
## iter 40 value 753.011257
## final value 752.821685
## converged
DryBean_TDA_KDE_5.60.5_n4_LrFit0
## Penalized Multinomial Regression
##
## 1759 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 1173, 1172, 1173
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.8135368 0.6823603
## 1e-04 0.8129660 0.6814350
## 1e-01 0.8180787 0.6887637
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 0.1.
DryBean_TDA_KDE_5.60.5_n4_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.8088737 0.6741923 Fold1
## 2 0.8276451 0.7026546 Fold3
## 3 0.8177172 0.6894441 Fold2
nb_tda_kde_5.60.5_n4_lr_fit_re<-DryBean_TDA_KDE_5.60.5_n4_LrFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n4_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## HOROZ -0.007812297 -6.553801e-04 0.05050997 0.5803191 0.6367674
## SEKER 0.056022025 1.495216e-02 0.15697171 -2.3339996 -2.3837193
## SIRA -0.018745715 -8.518868e-05 0.08567921 0.5231472 0.8213527
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## HOROZ -0.01184771 -0.001732693 0.001596173 -1.588726 -0.1989763
## SEKER 0.06168213 -0.121316864 -0.013734111 4.069019 0.7787332
## SIRA -0.64058312 0.642991220 0.001135857 -1.793984 4.4085934
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## HOROZ -0.01044365 -0.02671530 -0.01586829 8.063949e-05 -8.597689e-05
## SEKER 0.05040623 0.09480424 0.11543286 1.886741e-04 1.117565e-03
## SIRA -0.02184607 -0.23800259 -0.16046994 -4.177328e-04 -2.110666e-03
## ShapeFactor3 ShapeFactor4
## HOROZ -0.02083936 -0.02900588
## SEKER 0.17352208 0.13642031
## SIRA -0.37720992 -0.22753275
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## HOROZ 5.691889e-07 0.0036282611 0.0004722981 0.0001435033 1.031957e-05
## SEKER 1.786322e-05 0.0019558601 0.0063338673 0.0020005082 1.918749e-03
## SIRA 6.777809e-06 0.0008724001 0.0026992888 0.0012540549 5.885693e-04
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## HOROZ 1.604190e-06 6.878442e-07 0.0035739428 5.246673e-05 2.207784e-07
## SEKER 2.112890e-05 1.026568e-05 0.0019926361 1.980605e-03 1.343189e-05
## SIRA 1.385025e-05 6.479689e-06 0.0008887527 7.427780e-04 4.756165e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## HOROZ 3.819824e-07 2.470640e-07 2.307064e-07 8.553245e-09 1.675254e-10
## SEKER 1.772247e-05 1.698536e-05 1.631721e-05 1.719567e-07 6.755174e-08
## SIRA 6.717698e-06 5.605193e-06 5.127042e-06 7.957354e-08 1.912752e-08
## ShapeFactor3 ShapeFactor4
## HOROZ 3.483254e-08 4.655267e-07
## SEKER 1.484638e-05 1.787678e-05
## SIRA 4.383074e-06 6.765515e-06
##
## Residual Deviance: 1505.643
## AIC: 1607.643
vip(DryBean_TDA_KDE_5.60.5_n4_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n4_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n4_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n4_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n4_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n4_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 0 0 0 984 3 13 77
## HOROZ 0 0 3 1 447 0 1
## SEKER 15 9 1 11 0 569 10
## SIRA 381 147 485 67 128 26 702
##
## Overall Statistics
##
## Accuracy : 0.6623
## 95% CI : (0.6475, 0.6768)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5784
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9257
## Specificity 1.00000 1.00000 1.0000 0.9692
## Pos Pred Value NaN NaN NaN 0.9136
## Neg Pred Value 0.90294 0.96176 0.8801 0.9737
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2412
## Detection Prevalence 0.00000 0.00000 0.0000 0.2640
## Balanced Accuracy 0.50000 0.50000 0.5000 0.9474
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.7734 0.9359 0.8886
## Specificity 0.9986 0.9868 0.6249
## Pos Pred Value 0.9889 0.9252 0.3626
## Neg Pred Value 0.9639 0.9887 0.9590
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1096 0.1395 0.1721
## Detection Prevalence 0.1108 0.1507 0.4745
## Balanced Accuracy 0.8860 0.9613 0.7568
nb_tda_kde_5.60.5_n4_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 0 0 0 984 3 13 77
## HOROZ 0 0 3 1 447 0 1
## SEKER 15 9 1 11 0 569 10
## SIRA 381 147 485 67 128 26 702
##
## Overall Statistics
##
## Accuracy : 0.6623
## 95% CI : (0.6475, 0.6768)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5784
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.9257
## Specificity 1.00000 1.00000 1.0000 0.9692
## Pos Pred Value NaN NaN NaN 0.9136
## Neg Pred Value 0.90294 0.96176 0.8801 0.9737
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2412
## Detection Prevalence 0.00000 0.00000 0.0000 0.2640
## Balanced Accuracy 0.50000 0.50000 0.5000 0.9474
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.7734 0.9359 0.8886
## Specificity 0.9986 0.9868 0.6249
## Pos Pred Value 0.9889 0.9252 0.3626
## Neg Pred Value 0.9639 0.9887 0.9590
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1096 0.1395 0.1721
## Detection Prevalence 0.1108 0.1507 0.4745
## Balanced Accuracy 0.8860 0.9613 0.7568
nb_tda_kde_5.60.5_n4_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.6622549 0.5784458 0.6475082 0.6767689 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n4_db_lr_cf0_ov_acc<-nb_tda_kde_5.60.5_n4_db_lr_cf0$overall[1]
nb_tda_kde_5.60.5_n4_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.9256820 0.9691747 0.9136490 0.9736930 0.9136490
## Class: HOROZ 0.7733564 0.9985722 0.9889381 0.9638920 0.9889381
## Class: SEKER 0.9358553 0.9867512 0.9252033 0.9887446 0.9252033
## Class: SIRA 0.8886076 0.6249240 0.3626033 0.9589552 0.3626033
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.0000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.0000000
## Class: CALI 0.0000000 NA 0.11985294 0.0000000
## Class: DERMASON 0.9256820 0.9196262 0.26053922 0.2411765
## Class: HOROZ 0.7733564 0.8679612 0.14166667 0.1095588
## Class: SEKER 0.9358553 0.9304988 0.14901961 0.1394608
## Class: SIRA 0.8886076 0.5150404 0.19362745 0.1720588
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.0000000 0.5000000
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.0000000 0.5000000
## Class: DERMASON 0.2639706 0.9474284
## Class: HOROZ 0.1107843 0.8859643
## Class: SEKER 0.1507353 0.9613032
## Class: SIRA 0.4745098 0.7567658
nb_tda_kde_5.60.5_n4_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n4_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_lr_n4_3_fold<-(db_lr_fit_re - nb_tda_kde_5.60.5_n4_lr_fit_re)
diff_drybean_tda_kde_5.60.5_lr_n4_3_fold
## Accuracy
## 1 0.12217162
## 2 0.09649722
## 3 0.10581961
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n4_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n4_3_fold_odds.left<-bst_tda_kde_5.60.5_lr.n4_3_fold$probLeft/bst_tda_kde_5.60.5_lr.n4_3_fold$probRight
bst_tda_kde_5.60.5_lr.n4_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n4_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.009833333
##
## $winRight
## [1] 0.9901667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n4_3_fold
## $left
## [1] 0.002666858
##
## $rope
## [1] 0.001183611
##
## $right
## [1] 0.9961495
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold))
#bf_tda_kde_5.60.5_lr.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_lr_n4_3_fold)
## t = 14.415, df = 2, p-value = 0.004778
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.07587747 0.14044816
## sample estimates:
## mean of x
## 0.1081628
### Test set diff
diff_drybean_tda_kde_5.60.5_lr.n4_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n4_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_lr.n4_test
## Accuracy
## 0.2651961
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n4_test),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n4_test_odds.left<-bst_tda_kde_5.60.5_lr.n4_test$probLeft/bst_tda_kde_5.60.5_lr.n4_test$probRight
bst_tda_kde_5.60.5_lr.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n4_test),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1584333
##
## $winRight
## [1] 0.8415667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_lr.n4_test)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr.n4_test)) #bf_tda_pca_5.60.5_lr.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n4_test))
##Node5
DryBean_TDA_KDE_5.60.5_n5_LrFit0 <- train(as.factor(Class) ~ .,
data = tda.m_kde_dry_bean_dataset_5.60.5.n5.vec,
family = 'binomial',
method = 'multinom',
trControl = fitControl,
metric='Accuracy')
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'HOROZ' is empty
## # weights: 54 (34 variable)
## initial value 565.785329
## iter 10 value 352.337396
## iter 20 value 277.590063
## iter 30 value 276.810897
## iter 40 value 274.674321
## iter 50 value 273.307633
## iter 60 value 271.017584
## iter 70 value 270.838915
## iter 80 value 270.802325
## iter 90 value 270.732885
## iter 100 value 270.297104
## final value 270.297104
## stopped after 100 iterations
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'HOROZ' is empty
## # weights: 54 (34 variable)
## initial value 565.785329
## iter 10 value 352.353750
## iter 20 value 284.972032
## final value 284.969223
## converged
## Warning in nnet::multinom(.outcome ~ ., data = dat, decay = param$decay, :
## group 'HOROZ' is empty
## # weights: 54 (34 variable)
## initial value 565.785329
## iter 10 value 352.337413
## iter 20 value 277.610569
## iter 30 value 276.873824
## iter 40 value 275.212684
## iter 50 value 274.513727
## iter 60 value 274.315250
## iter 70 value 274.311017
## iter 80 value 274.310530
## iter 90 value 274.310289
## iter 100 value 274.310095
## final value 274.310095
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 716.714185
## iter 10 value 405.259148
## iter 20 value 291.766949
## iter 30 value 273.802517
## iter 40 value 272.493368
## iter 50 value 271.561546
## iter 60 value 269.278114
## iter 70 value 269.000075
## iter 80 value 268.936560
## iter 90 value 266.698896
## iter 100 value 265.946636
## final value 265.946636
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 716.714185
## iter 10 value 405.261860
## iter 20 value 294.767039
## iter 30 value 283.649191
## iter 40 value 282.839990
## iter 50 value 282.753839
## iter 50 value 282.753836
## iter 50 value 282.753836
## final value 282.753836
## converged
## # weights: 72 (51 variable)
## initial value 716.714185
## iter 10 value 405.259151
## iter 20 value 291.775701
## iter 30 value 273.837510
## iter 40 value 272.585669
## iter 50 value 271.799251
## iter 60 value 270.608257
## iter 70 value 270.579744
## iter 80 value 270.377924
## iter 90 value 270.253583
## iter 100 value 270.043452
## final value 270.043452
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 715.327890
## iter 10 value 392.005795
## iter 20 value 283.756538
## iter 30 value 272.605766
## iter 40 value 271.271072
## iter 50 value 267.632938
## iter 60 value 267.048400
## iter 70 value 266.962960
## iter 80 value 266.717552
## iter 90 value 265.803091
## iter 100 value 265.698662
## final value 265.698662
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 715.327890
## iter 10 value 392.008683
## iter 20 value 293.296420
## iter 30 value 285.689908
## iter 40 value 285.018282
## final value 284.928945
## converged
## # weights: 72 (51 variable)
## initial value 715.327890
## iter 10 value 392.005798
## iter 20 value 283.774483
## iter 30 value 272.643939
## iter 40 value 271.366861
## iter 50 value 268.845499
## iter 60 value 268.568973
## iter 70 value 268.487423
## iter 80 value 268.228353
## iter 90 value 268.120271
## iter 100 value 268.005604
## final value 268.005604
## stopped after 100 iterations
## # weights: 72 (51 variable)
## initial value 1072.991836
## iter 10 value 555.683162
## iter 20 value 426.452643
## iter 30 value 416.550856
## iter 40 value 413.447536
## iter 50 value 413.072117
## iter 60 value 412.305407
## iter 70 value 412.063181
## iter 80 value 411.540277
## iter 90 value 411.525503
## iter 100 value 411.252516
## final value 411.252516
## stopped after 100 iterations
DryBean_TDA_KDE_5.60.5_n5_LrFit0
## Penalized Multinomial Regression
##
## 774 samples
## 16 predictor
## 4 classes: 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 515, 517, 516
## Resampling results across tuning parameters:
##
## decay Accuracy Kappa
## 0e+00 0.7338124 0.5191699
## 1e-04 0.7364364 0.5212859
## 1e-01 0.7261053 0.4909906
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was decay = 1e-04.
DryBean_TDA_KDE_5.60.5_n5_LrFit0$resample
## Accuracy Kappa Resample
## 1 0.7315175 0.5164035 Fold2
## 2 0.7297297 0.5111495 Fold1
## 3 0.7480620 0.5363048 Fold3
nb_tda_kde_5.60.5_n5_lr_fit_re<-DryBean_TDA_KDE_5.60.5_n5_LrFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n5_LrFit0)
## Call:
## nnet::multinom(formula = .outcome ~ ., data = dat, decay = param$decay,
## family = "binomial")
##
## Coefficients:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## HOROZ 0.02531359 -0.077793336 -0.33133452 0.1140418 -8.000236
## SEKER -8.12689964 0.021407043 0.26570689 -3.7240218 -4.867661
## SIRA -16.68229175 -0.001321159 -0.05519992 0.1514487 0.747400
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## HOROZ 0.5499971 -0.1842233 0.078741126 6.8849288 31.581368
## SEKER -19.5974641 -37.7644759 -0.021620828 8.0150668 -0.723981
## SIRA -31.2205936 50.2989311 -0.002785746 0.9404719 12.680554
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## HOROZ 0.03765175 -0.04419988 0.0691424 0.000515291 0.0004973407
## SEKER -5.87152976 -0.71956338 1.9452989 -0.260678569 0.0883285824
## SIRA -8.86067944 -78.24197406 -32.9979781 -0.206788247 -0.3889296212
## ShapeFactor3 ShapeFactor4
## HOROZ 0.1570804 0.217960
## SEKER 11.6490272 -4.415946
## SIRA -51.6522937 -28.500255
##
## Std. Errors:
## (Intercept) Area Perimeter MajorAxisLength MinorAxisLength
## HOROZ 1.403890e-09 1.954579e-05 1.180756e-06 2.793500e-07 1.168078e-07
## SEKER 8.842265e-07 2.663973e-03 6.266539e-04 1.094858e-04 8.361600e-05
## SIRA 1.125842e-05 1.362706e-03 5.240345e-03 2.593730e-03 2.773576e-04
## AspectRation Eccentricity ConvexArea EquivDiameter Extent
## HOROZ 3.297510e-09 1.434706e-09 1.957882e-05 1.838605e-07 5.290888e-09
## SEKER 1.186495e-06 5.754347e-07 2.626208e-03 9.488256e-05 4.054459e-07
## SIRA 2.831257e-05 1.367455e-05 1.397753e-03 1.239471e-03 7.866055e-06
## Solidity roundness Compactness ShapeFactor1 ShapeFactor2
## HOROZ 1.513840e-09 8.135882e-10 1.040036e-09 1.586784e-11 3.434912e-12
## SEKER 8.209745e-07 9.464321e-07 7.633997e-07 8.958120e-09 3.045089e-09
## SIRA 1.116065e-05 7.359997e-06 5.899797e-06 1.468320e-07 9.131187e-09
## ShapeFactor3 ShapeFactor4
## HOROZ 8.122022e-10 1.703429e-09
## SEKER 6.607627e-07 8.775611e-07
## SIRA 2.062744e-06 1.121153e-05
##
## Residual Deviance: 822.505
## AIC: 924.505
vip(DryBean_TDA_KDE_5.60.5_n5_LrFit0,50) + ggtitle("DryBean_TDA_KDE_5.60.5_n5_Lr1Fit TDA-Assited LR")
# Predict outcome using DryBean_TDA_KDE_5.60.5_n5_LrFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n5_LrFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n5_db_lr_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in levels(reference) != levels(data): Levels are not in the same order
## for reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n5_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 11 1 5 876 0 20 153
## HOROZ 218 122 473 157 578 4 341
## SEKER 151 33 10 10 0 577 18
## SIRA 16 0 1 20 0 7 278
##
## Overall Statistics
##
## Accuracy : 0.5659
## 95% CI : (0.5506, 0.5812)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4718
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.8241
## Specificity 1.00000 1.00000 1.0000 0.9370
## Pos Pred Value NaN NaN NaN 0.8218
## Neg Pred Value 0.90294 0.96176 0.8801 0.9380
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2147
## Detection Prevalence 0.00000 0.00000 0.0000 0.2613
## Balanced Accuracy 0.50000 0.50000 0.5000 0.8806
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 1.0000 0.9490 0.35190
## Specificity 0.6245 0.9361 0.98663
## Pos Pred Value 0.3053 0.7222 0.86335
## Neg Pred Value 1.0000 0.9906 0.86376
## Prevalence 0.1417 0.1490 0.19363
## Detection Rate 0.1417 0.1414 0.06814
## Detection Prevalence 0.4640 0.1958 0.07892
## Balanced Accuracy 0.8123 0.9425 0.66926
nb_tda_kde_5.60.5_n5_db_lr_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 0 0 0 0 0 0 0
## BOMBAY 0 0 0 0 0 0 0
## CALI 0 0 0 0 0 0 0
## DERMASON 11 1 5 876 0 20 153
## HOROZ 218 122 473 157 578 4 341
## SEKER 151 33 10 10 0 577 18
## SIRA 16 0 1 20 0 7 278
##
## Overall Statistics
##
## Accuracy : 0.5659
## 95% CI : (0.5506, 0.5812)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.4718
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.00000 0.00000 0.0000 0.8241
## Specificity 1.00000 1.00000 1.0000 0.9370
## Pos Pred Value NaN NaN NaN 0.8218
## Neg Pred Value 0.90294 0.96176 0.8801 0.9380
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.00000 0.00000 0.0000 0.2147
## Detection Prevalence 0.00000 0.00000 0.0000 0.2613
## Balanced Accuracy 0.50000 0.50000 0.5000 0.8806
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 1.0000 0.9490 0.35190
## Specificity 0.6245 0.9361 0.98663
## Pos Pred Value 0.3053 0.7222 0.86335
## Neg Pred Value 1.0000 0.9906 0.86376
## Prevalence 0.1417 0.1490 0.19363
## Detection Rate 0.1417 0.1414 0.06814
## Detection Prevalence 0.4640 0.1958 0.07892
## Balanced Accuracy 0.8123 0.9425 0.66926
nb_tda_kde_5.60.5_n5_db_lr_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5659314 0.4717652 0.5505579 0.5812104 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n5_db_lr_cf0_ov_acc<-nb_tda_kde_5.60.5_n5_db_lr_cf0$overall[1]
nb_tda_kde_5.60.5_n5_db_lr_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.0000000 1.0000000 NaN 0.9029412 NA
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.0000000 1.0000000 NaN 0.8801471 NA
## Class: DERMASON 0.8240828 0.9370235 0.8217636 0.9379562 0.8217636
## Class: HOROZ 1.0000000 0.6245003 0.3053354 1.0000000 0.3053354
## Class: SEKER 0.9490132 0.9360599 0.7221527 0.9905517 0.7221527
## Class: SIRA 0.3518987 0.9866261 0.8633540 0.8637573 0.8633540
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.0000000 NA 0.09705882 0.00000000
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.0000000 NA 0.11985294 0.00000000
## Class: DERMASON 0.8240828 0.8229216 0.26053922 0.21470588
## Class: HOROZ 1.0000000 0.4678268 0.14166667 0.14166667
## Class: SEKER 0.9490132 0.8201848 0.14901961 0.14142157
## Class: SIRA 0.3518987 0.5000000 0.19362745 0.06813725
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.00000000 0.5000000
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.00000000 0.5000000
## Class: DERMASON 0.26127451 0.8805532
## Class: HOROZ 0.46397059 0.8122501
## Class: SEKER 0.19583333 0.9425365
## Class: SIRA 0.07892157 0.6692624
nb_tda_kde_5.60.5_n5_db_lr_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n5_db_lr_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_lr_n5_3_fold<-(db_lr_fit_re - nb_tda_kde_5.60.5_n5_lr_fit_re)
diff_drybean_tda_kde_5.60.5_lr_n5_3_fold
## Accuracy
## 1 0.1995278
## 2 0.1944125
## 3 0.1754748
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n5_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n5_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n5_3_fold_odds.left<-bst_tda_kde_5.60.5_lr.n5_3_fold$probLeft/bst_tda_kde_5.60.5_lr.n5_3_fold$probRight
bst_tda_kde_5.60.5_lr.n5_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n5_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n5_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0085
##
## $winRight
## [1] 0.9915
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n5_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n5_3_fold
## $left
## [1] 0.0008913453
##
## $rope
## [1] 0.0002086293
##
## $right
## [1] 0.9989
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold))
#bf_tda_kde_5.60.5_lr.n5_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_lr_n5_3_fold)
## t = 25.945, df = 2, p-value = 0.001482
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.1583281 0.2212820
## sample estimates:
## mean of x
## 0.1898051
### Test set diff
diff_drybean_tda_kde_5.60.5_lr.n5_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n5_db_lr_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_lr.n5_test
## Accuracy
## 0.3615196
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n5_test),-0.01,0.01)
bst_tda_kde_5.60.5_lr.n5_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_lr.n5_test_odds.left<-bst_tda_kde_5.60.5_lr.n5_test$probLeft/bst_tda_kde_5.60.5_lr.n5_test$probRight
bst_tda_kde_5.60.5_lr.n5_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_lr.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n5_test),-0.01,0.01)
bsr_tda_kde_5.60.5_lr.n5_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1567667
##
## $winRight
## [1] 0.8432333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_lr.n5_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n5_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_lr.n5_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_lr.n5_test)))
#BayesFactor
#bf_tda_kde_5.60.5_lr.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_lr.n5_test)) #bf_tda_pca_5.60.5_lr.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_lr.n5_test))
#naiveBayes
dryBeanNbFit <- train(as.factor(Class) ~ ., data = Dry_Bean_DatasetTrain,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
dryBeanNbFit
## Naive Bayes
##
## 9531 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 6355, 6353, 6354
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.9000109 0.8792485
## TRUE 0.9028444 0.8825798
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.
dryBeanNbFit$resample
## Accuracy Kappa Resample
## 1 0.9080605 0.8888929 Fold1
## 2 0.8977344 0.8763074 Fold2
## 3 0.9027384 0.8825389 Fold3
db_nb_fit_re<-dryBeanNbFit$resample[1]
summary(dryBeanNbFit)
## Length Class Mode
## apriori 7 table numeric
## tables 16 -none- list
## levels 7 -none- character
## call 6 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 7 -none- character
## param 0 -none- list
#varImp (dryBeanNbFit)
# Predict outcome using model from training data based on testing data
predictions <- predict(dryBeanNbFit, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
# Create confusion matrix to assess model fit/performance on test data
db_nb_cf<-confusionMatrix(data=predictions, as.factor(Dry_Bean_DatasetTest$Class))
db_nb_cf
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 299 0 38 0 1 5 6
## BOMBAY 1 156 0 0 0 0 0
## CALI 68 0 437 0 13 0 0
## DERMASON 0 0 0 951 4 9 68
## HOROZ 4 0 11 1 550 0 18
## SEKER 3 0 1 22 0 567 9
## SIRA 21 0 2 89 10 27 689
##
## Overall Statistics
##
## Accuracy : 0.8944
## 95% CI : (0.8845, 0.9036)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8723
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.75505 1.00000 0.8937 0.8946
## Specificity 0.98643 0.99975 0.9774 0.9732
## Pos Pred Value 0.85673 0.99363 0.8436 0.9215
## Neg Pred Value 0.97400 1.00000 0.9854 0.9633
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07328 0.03824 0.1071 0.2331
## Detection Prevalence 0.08554 0.03848 0.1270 0.2529
## Balanced Accuracy 0.87074 0.99987 0.9356 0.9339
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9516 0.9326 0.8722
## Specificity 0.9903 0.9899 0.9547
## Pos Pred Value 0.9418 0.9419 0.8222
## Neg Pred Value 0.9920 0.9882 0.9688
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1348 0.1390 0.1689
## Detection Prevalence 0.1431 0.1475 0.2054
## Balanced Accuracy 0.9709 0.9612 0.9134
db_nb_cf$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8943627 0.8722759 0.8845246 0.9036320 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_nb_cf_ov_acc<-db_nb_cf$overall[1]
db_nb_cf$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.7550505 0.9864278 0.8567335 0.9740016 0.8567335
## Class: BOMBAY 1.0000000 0.9997452 0.9936306 1.0000000 0.9936306
## Class: CALI 0.8936605 0.9774436 0.8436293 0.9854015 0.8436293
## Class: DERMASON 0.8946378 0.9731521 0.9215116 0.9632546 0.9215116
## Class: HOROZ 0.9515571 0.9902913 0.9417808 0.9919908 0.9417808
## Class: SEKER 0.9325658 0.9899194 0.9418605 0.9882116 0.9418605
## Class: SIRA 0.8721519 0.9547112 0.8221957 0.9688464 0.8221957
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7550505 0.8026846 0.09705882 0.07328431
## Class: BOMBAY 1.0000000 0.9968051 0.03823529 0.03823529
## Class: CALI 0.8936605 0.8679245 0.11985294 0.10710784
## Class: DERMASON 0.8946378 0.9078759 0.26053922 0.23308824
## Class: HOROZ 0.9515571 0.9466437 0.14166667 0.13480392
## Class: SEKER 0.9325658 0.9371901 0.14901961 0.13897059
## Class: SIRA 0.8721519 0.8464373 0.19362745 0.16887255
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.08553922 0.8707392
## Class: BOMBAY 0.03848039 0.9998726
## Class: CALI 0.12696078 0.9355521
## Class: DERMASON 0.25294118 0.9338950
## Class: HOROZ 0.14313725 0.9709242
## Class: SEKER 0.14754902 0.9612426
## Class: SIRA 0.20539216 0.9134316
db_nb_cf_pre_rec_f1<-db_nb_cf$byClass[5:7]
##With TDA PCA filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_PC_5.60.5_n1_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n1.vec,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
DryBean_TDA_PC_5.60.5_n1_NbFit0
## Naive Bayes
##
## 6835 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 4555, 4557, 4558
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.8307209 0.7426375
## TRUE 0.8348213 0.7472254
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.
DryBean_TDA_PC_5.60.5_n1_NbFit0$resample
## Accuracy Kappa Resample
## 1 0.8372807 0.7494038 Fold1
## 2 0.8235294 0.7320441 Fold2
## 3 0.8436539 0.7602284 Fold3
db_tda_pc_5.60.5_n1_nb_fit_re<-DryBean_TDA_PC_5.60.5_n1_NbFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n1_NbFit0)
## Length Class Mode
## apriori 6 table numeric
## tables 16 -none- list
## levels 6 -none- character
## call 6 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 6 -none- character
## param 0 -none- list
# Predict outcome using DryBean_TDA_PC_5.60.5_n1_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n1_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4053
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n1_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n1_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 254 0 68 0 279 29 100
## BOMBAY 0 0 0 0 0 0 0
## CALI 42 38 185 0 239 22 14
## DERMASON 0 0 0 836 2 8 29
## HOROZ 97 118 234 71 56 1 65
## SEKER 0 0 0 20 0 531 4
## SIRA 3 0 2 136 2 17 578
##
## Overall Statistics
##
## Accuracy : 0.598
## 95% CI : (0.5828, 0.6131)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5176
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.64141 0.00000 0.37832 0.7865
## Specificity 0.87079 1.00000 0.90114 0.9871
## Pos Pred Value 0.34795 NaN 0.34259 0.9554
## Neg Pred Value 0.95761 0.96176 0.91412 0.9292
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.06225 0.00000 0.04534 0.2049
## Detection Prevalence 0.17892 0.00000 0.13235 0.2145
## Balanced Accuracy 0.75610 0.50000 0.63973 0.8868
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.09689 0.8734 0.7316
## Specificity 0.83267 0.9931 0.9514
## Pos Pred Value 0.08723 0.9568 0.7832
## Neg Pred Value 0.84817 0.9782 0.9366
## Prevalence 0.14167 0.1490 0.1936
## Detection Rate 0.01373 0.1301 0.1417
## Detection Prevalence 0.15735 0.1360 0.1809
## Balanced Accuracy 0.46478 0.9332 0.8415
db_tda_pc_5.60.5_n1_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 254 0 68 0 279 29 100
## BOMBAY 0 0 0 0 0 0 0
## CALI 42 38 185 0 239 22 14
## DERMASON 0 0 0 836 2 8 29
## HOROZ 97 118 234 71 56 1 65
## SEKER 0 0 0 20 0 531 4
## SIRA 3 0 2 136 2 17 578
##
## Overall Statistics
##
## Accuracy : 0.598
## 95% CI : (0.5828, 0.6131)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.5176
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.64141 0.00000 0.37832 0.7865
## Specificity 0.87079 1.00000 0.90114 0.9871
## Pos Pred Value 0.34795 NaN 0.34259 0.9554
## Neg Pred Value 0.95761 0.96176 0.91412 0.9292
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.06225 0.00000 0.04534 0.2049
## Detection Prevalence 0.17892 0.00000 0.13235 0.2145
## Balanced Accuracy 0.75610 0.50000 0.63973 0.8868
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.09689 0.8734 0.7316
## Specificity 0.83267 0.9931 0.9514
## Pos Pred Value 0.08723 0.9568 0.7832
## Neg Pred Value 0.84817 0.9782 0.9366
## Prevalence 0.14167 0.1490 0.1936
## Detection Rate 0.01373 0.1301 0.1417
## Detection Prevalence 0.15735 0.1360 0.1809
## Balanced Accuracy 0.46478 0.9332 0.8415
db_tda_pc_5.60.5_n1_db_nb_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.5980392 0.5176331 0.5828065 0.6131314 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n1_db_nb_cf0_ov_acc<-db_tda_pc_5.60.5_n1_db_nb_cf0$overall[1]
db_tda_pc_5.60.5_n1_db_nb_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value
## Class: BARBUNYA 0.64141414 0.8707926 0.34794521 0.9576119
## Class: BOMBAY 0.00000000 1.0000000 NaN 0.9617647
## Class: CALI 0.37832311 0.9011417 0.34259259 0.9141243
## Class: DERMASON 0.78645343 0.9870733 0.95542857 0.9291732
## Class: HOROZ 0.09688581 0.8326670 0.08722741 0.8481675
## Class: SEKER 0.87335526 0.9930876 0.95675676 0.9781560
## Class: SIRA 0.73164557 0.9513678 0.78319783 0.9365649
## Precision Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.34794521 0.64141414 0.45115453 0.09705882 0.06225490
## Class: BOMBAY NA 0.00000000 NA 0.03823529 0.00000000
## Class: CALI 0.34259259 0.37832311 0.35957240 0.11985294 0.04534314
## Class: DERMASON 0.95542857 0.78645343 0.86274510 0.26053922 0.20490196
## Class: HOROZ 0.08722741 0.09688581 0.09180328 0.14166667 0.01372549
## Class: SEKER 0.95675676 0.87335526 0.91315563 0.14901961 0.13014706
## Class: SIRA 0.78319783 0.73164557 0.75654450 0.19362745 0.14166667
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.1789216 0.7561034
## Class: BOMBAY 0.0000000 0.5000000
## Class: CALI 0.1323529 0.6397324
## Class: DERMASON 0.2144608 0.8867633
## Class: HOROZ 0.1573529 0.4647764
## Class: SEKER 0.1360294 0.9332214
## Class: SIRA 0.1808824 0.8415067
db_tda_pc_5.60.5_n1_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n1_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nb_n1_3_fold<-(db_nb_fit_re - db_tda_pc_5.60.5_n1_nb_fit_re)
diff_drybean_tda_pca_5.60.5_nb_n1_3_fold
## Accuracy
## 1 0.07077975
## 2 0.07420501
## 3 0.05908450
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nb.n1_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n1_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n1_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n1_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nb.n1_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nb.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nb.n1_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.007833333
##
## $winRight
## [1] 0.9921667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nb.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nb.n1_3_fold
## $left
## [1] 0.002278856
##
## $rope
## [1] 0.001819159
##
## $right
## [1] 0.995902
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold))
#bf_tda_pca_5.60.5_nb.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nb_n1_3_fold)
## t = 14.861, df = 2, p-value = 0.004498
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.04832827 0.08771791
## sample estimates:
## mean of x
## 0.06802309
### Test set diff
diff_drybean_tda_pca_5.60.5_nb.n1_test<-(db_nb_cf_ov_acc - db_tda_pc_5.60.5_n1_db_nb_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nb.n1_test
## Accuracy
## 0.2963235
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n1_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nb.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n1_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n1_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n1_test$probRight
bst_dbf_db_tda_pca_5.60.5_nb.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nb.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n1_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nb.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1601667
##
## $winRight
## [1] 0.8398333
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nb.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n1_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nb.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb.n1_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb.n1_test)) #bf_tda_pca_5.60.5_nb.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n1_test))
##Node2
DryBean_TDA_PC_5.60.5_n2_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n2.vec,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
DryBean_TDA_PC_5.60.5_n2_NbFit0
## Naive Bayes
##
## 8024 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5350, 5349, 5349
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.8541876 0.8118884
## TRUE 0.8589226 0.8176145
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.
DryBean_TDA_PC_5.60.5_n2_NbFit0$resample
## Accuracy Kappa Resample
## 1 0.8541511 0.8115802 Fold1
## 2 0.8624299 0.8221305 Fold2
## 3 0.8601869 0.8191326 Fold3
db_tda_pc_5.60.5_n2_nb_fit_re<-DryBean_TDA_PC_5.60.5_n2_NbFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n2_NbFit0)
## Length Class Mode
## apriori 6 table numeric
## tables 16 -none- list
## levels 6 -none- character
## call 6 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 6 -none- character
## param 0 -none- list
#Predict outcome using DryBean_TDA_PC_5.60.5_n2_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n2_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n2_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n2_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 300 2 67 0 3 2 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 68 80 412 0 14 0 6
## DERMASON 0 64 0 985 5 52 89
## HOROZ 4 3 7 1 551 0 19
## SEKER 5 7 1 21 0 541 28
## SIRA 19 0 2 56 5 13 645
##
## Overall Statistics
##
## Accuracy : 0.8417
## 95% CI : (0.8301, 0.8527)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.807
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.75758 0.00000 0.8425 0.9266
## Specificity 0.97910 1.00000 0.9532 0.9304
## Pos Pred Value 0.79576 NaN 0.7103 0.8243
## Neg Pred Value 0.97408 0.96176 0.9780 0.9730
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07353 0.00000 0.1010 0.2414
## Detection Prevalence 0.09240 0.00000 0.1422 0.2929
## Balanced Accuracy 0.86834 0.50000 0.8979 0.9285
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9533 0.8898 0.8165
## Specificity 0.9903 0.9821 0.9711
## Pos Pred Value 0.9419 0.8972 0.8716
## Neg Pred Value 0.9923 0.9807 0.9566
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1350 0.1326 0.1581
## Detection Prevalence 0.1434 0.1478 0.1814
## Balanced Accuracy 0.9718 0.9360 0.8938
db_tda_pc_5.60.5_n2_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 300 2 67 0 3 2 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 68 80 412 0 14 0 6
## DERMASON 0 64 0 985 5 52 89
## HOROZ 4 3 7 1 551 0 19
## SEKER 5 7 1 21 0 541 28
## SIRA 19 0 2 56 5 13 645
##
## Overall Statistics
##
## Accuracy : 0.8417
## 95% CI : (0.8301, 0.8527)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.807
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.75758 0.00000 0.8425 0.9266
## Specificity 0.97910 1.00000 0.9532 0.9304
## Pos Pred Value 0.79576 NaN 0.7103 0.8243
## Neg Pred Value 0.97408 0.96176 0.9780 0.9730
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07353 0.00000 0.1010 0.2414
## Detection Prevalence 0.09240 0.00000 0.1422 0.2929
## Balanced Accuracy 0.86834 0.50000 0.8979 0.9285
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9533 0.8898 0.8165
## Specificity 0.9903 0.9821 0.9711
## Pos Pred Value 0.9419 0.8972 0.8716
## Neg Pred Value 0.9923 0.9807 0.9566
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1350 0.1326 0.1581
## Detection Prevalence 0.1434 0.1478 0.1814
## Balanced Accuracy 0.9718 0.9360 0.8938
db_tda_pc_5.60.5_n2_db_nb_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8416667 0.8069643 0.8300987 0.8527430 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
db_tda_pc_5.60.5_n2_db_nb_cf0_ov_acc<-db_tda_pc_5.60.5_n2_db_nb_cf0$overall[1]
db_tda_pc_5.60.5_n2_db_nb_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.7575758 0.9790988 0.7957560 0.9740751 0.7957560
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.8425358 0.9532164 0.7103448 0.9780000 0.7103448
## Class: DERMASON 0.9266228 0.9303944 0.8242678 0.9729636 0.8242678
## Class: HOROZ 0.9532872 0.9902913 0.9418803 0.9922747 0.9418803
## Class: SEKER 0.8898026 0.9821429 0.8971808 0.9807305 0.8971808
## Class: SIRA 0.8164557 0.9711246 0.8716216 0.9565868 0.8716216
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7575758 0.7761966 0.09705882 0.07352941
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.8425358 0.7708138 0.11985294 0.10098039
## Class: DERMASON 0.9266228 0.8724535 0.26053922 0.24142157
## Class: HOROZ 0.9532872 0.9475494 0.14166667 0.13504902
## Class: SEKER 0.8898026 0.8934765 0.14901961 0.13259804
## Class: SIRA 0.8164557 0.8431373 0.19362745 0.15808824
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09240196 0.8683373
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.14215686 0.8978761
## Class: DERMASON 0.29289216 0.9285086
## Class: HOROZ 0.14338235 0.9717892
## Class: SEKER 0.14779412 0.9359727
## Class: SIRA 0.18137255 0.8937902
db_tda_pc_5.60.5_n2_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n2_db_nb_cf0$byClass[5:7]#
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nb_n2_3_fold<-(db_nb_fit_re - db_tda_pc_5.60.5_n2_nb_fit_re)
diff_drybean_tda_pca_5.60.5_nb_n2_3_fold
## Accuracy
## 1 0.05390937
## 2 0.03530452
## 3 0.04255152
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nb.n2_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n2_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n2_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n2_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nb.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nb.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nb.n2_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.0083
##
## $winRight
## [1] 0.9917
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nb.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nb.n2_3_fold
## $left
## [1] 0.006588867
##
## $rope
## [1] 0.009575758
##
## $right
## [1] 0.9838354
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold))
#bf_tda_pca_5.60.5_nb.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nb_n2_3_fold)
## t = 8.1122, df = 2, p-value = 0.01486
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.02062602 0.06721758
## sample estimates:
## mean of x
## 0.0439218
### Test set diff
diff_drybean_tda_pca_5.60.5_nb.n2_test<-(db_nb_cf_ov_acc - db_tda_pc_5.60.5_n2_db_nb_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nb.n2_test
## Accuracy
## 0.05269608
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n2_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nb.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n2_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n2_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n2_test$probRight
bst_dbf_db_tda_pca_5.60.5_nb.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nb.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n2_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nb.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1595333
##
## $winRight
## [1] 0.8404667
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nb.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n2_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nb.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb.n2_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb.n2_test)) #bf_tda_pca_5.60.5_nb.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n2_test))
##Node3
#DryBean_TDA_PC_5.60.5_n3_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n3.vec,
# method = 'nb',
# trControl = fitControl,
# metric='Accuracy')
#DryBean_TDA_PC_5.60.5_n3_NbFit0
#DryBean_TDA_PC_5.60.5_n3_NbFit0$resample
#db_tda_pc_5.60.5_n3_nb_fit_re<-DryBean_TDA_PC_5.60.5_n3_NbFit0$resample[1]
#summary(DryBean_TDA_PC_5.60.5_n3_NbFit0)
#Predict outcome using DryBean_TDA_PC_5.60.5_n3_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.60.5_n3_NbFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.60.5_n3_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.60.5_n3_db_nb_cf0
#db_tda_pc_5.60.5_n3_db_nb_cf0
#db_tda_pc_5.60.5_n3_db_nb_cf0$overall
#db_tda_pc_5.60.5_n3_db_nb_cf0_ov_acc<-db_tda_pc_5.60.5_n3_db_nb_cf0$overall[1]
#db_tda_pc_5.60.5_n3_db_nb_cf0$byClass
#db_tda_pc_5.60.5_n3_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n3_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_pca_5.60.5_nb_n3_3_fold<-(db_nb_fit_re - db_tda_pc_5.60.5_n3_nb_fit_re)
#diff_drybean_tda_pca_5.60.5_nb_n3_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n3_3_fold<-#BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n3_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_nb.n3_3_fold
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n3_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n3_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n3_3_fold$probRight
#bst_dbf_db_tda_pca_5.60.5_nb.n3_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_nb.n3_3_fold<-#BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n3_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_nb.n3_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_nb.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n3_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_nb.n3_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb_n3_3_fold))
#bf_tda_pca_5.60.5_nb.n3_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n3_3_fold))
### Test set diff
#diff_drybean_tda_pca_5.60.5_nb.n3_test<-(db_nb_cf_ov_acc - db_tda_pc_5.60.5_n3_db_nb_cf0_ov_acc)
#diff_drybean_tda_pca_5.60.5_nb.n3_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n3_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_nb.n3_test
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n3_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n3_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n3_test$probRight
#bst_dbf_db_tda_pca_5.60.5_nb.n3_test_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_nb.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n2_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_nb.n2_test
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_nb.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n3_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_nb.n3_test
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb.n3_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb.n3_test)) #bf_tda_pca_5.60.5_nb.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n3_test))
##Node4
DryBean_TDA_PC_5.60.5_n4_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n4.vec,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
DryBean_TDA_PC_5.60.5_n4_NbFit0
## Naive Bayes
##
## 894 samples
## 16 predictor
## 4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 595, 596, 597
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.9742953 0.9585317
## TRUE 0.9664727 0.9458031
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
## = 1.
DryBean_TDA_PC_5.60.5_n4_NbFit0$resample
## Accuracy Kappa Resample
## 1 0.9632107 0.9406650 Fold1
## 2 0.9765101 0.9620727 Fold2
## 3 0.9831650 0.9728574 Fold3
db_tda_pc_5.60.5_n4_nb_fit_re<-DryBean_TDA_PC_5.60.5_n4_NbFit0$resample[1]
summary(DryBean_TDA_PC_5.60.5_n4_NbFit0)
## Length Class Mode
## apriori 4 table numeric
## tables 16 -none- list
## levels 4 -none- character
## call 5 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 4 -none- character
## param 0 -none- list
# Predict outcome using DryBean_TDA_PC_5.60.5_n4_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_PC_5.60.5_n4_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4080
# Create confusion matrix to assess model fit/performance on test data
db_tda_pc_5.60.5_n4_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
db_tda_pc_5.60.5_n4_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 333 0 76 39 1 586 44
## BOMBAY 1 156 0 0 0 0 0
## CALI 25 0 354 0 3 0 0
## DERMASON 0 0 0 0 0 0 0
## HOROZ 37 0 59 1024 574 22 746
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3473
## 95% CI : (0.3327, 0.3621)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.255
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.84091 1.00000 0.72393 0.0000
## Specificity 0.79750 0.99975 0.99220 1.0000
## Pos Pred Value 0.30862 0.99363 0.92670 NaN
## Neg Pred Value 0.97901 1.00000 0.96349 0.7395
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.08162 0.03824 0.08676 0.0000
## Detection Prevalence 0.26446 0.03848 0.09363 0.0000
## Balanced Accuracy 0.81921 0.99987 0.85806 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9931 0.000 0.0000
## Specificity 0.4609 1.000 1.0000
## Pos Pred Value 0.2331 NaN NaN
## Neg Pred Value 0.9975 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1407 0.000 0.0000
## Detection Prevalence 0.6034 0.000 0.0000
## Balanced Accuracy 0.7270 0.500 0.5000
db_tda_pc_5.60.5_n4_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 333 0 76 39 1 586 44
## BOMBAY 1 156 0 0 0 0 0
## CALI 25 0 354 0 3 0 0
## DERMASON 0 0 0 0 0 0 0
## HOROZ 37 0 59 1024 574 22 746
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3473
## 95% CI : (0.3327, 0.3621)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.255
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.84091 1.00000 0.72393 0.0000
## Specificity 0.79750 0.99975 0.99220 1.0000
## Pos Pred Value 0.30862 0.99363 0.92670 NaN
## Neg Pred Value 0.97901 1.00000 0.96349 0.7395
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.08162 0.03824 0.08676 0.0000
## Detection Prevalence 0.26446 0.03848 0.09363 0.0000
## Balanced Accuracy 0.81921 0.99987 0.85806 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9931 0.000 0.0000
## Specificity 0.4609 1.000 1.0000
## Pos Pred Value 0.2331 NaN NaN
## Neg Pred Value 0.9975 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1407 0.000 0.0000
## Detection Prevalence 0.6034 0.000 0.0000
## Balanced Accuracy 0.7270 0.500 0.5000
db_tda_pc_5.60.5_n4_db_nb_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.473039e-01 2.550431e-01 3.326858e-01 3.621410e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 1.093830e-34 NaN
db_tda_pc_5.60.5_n4_db_nb_cf0_ov_acc<-db_tda_pc_5.60.5_n4_db_nb_cf0$overall[1]
db_tda_pc_5.60.5_n4_db_nb_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8409091 0.7975027 0.3086191 0.9790070 0.3086191
## Class: BOMBAY 1.0000000 0.9997452 0.9936306 1.0000000 0.9936306
## Class: CALI 0.7239264 0.9922027 0.9267016 0.9634938 0.9267016
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.9930796 0.4608795 0.2331438 0.9975278 0.2331438
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8409091 0.4515254 0.09705882 0.08161765
## Class: BOMBAY 1.0000000 0.9968051 0.03823529 0.03823529
## Class: CALI 0.7239264 0.8128588 0.11985294 0.08676471
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.9930796 0.3776316 0.14166667 0.14068627
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.26446078 0.8192059
## Class: BOMBAY 0.03848039 0.9998726
## Class: CALI 0.09362745 0.8580646
## Class: DERMASON 0.00000000 0.5000000
## Class: HOROZ 0.60343137 0.7269795
## Class: SEKER 0.00000000 0.5000000
## Class: SIRA 0.00000000 0.5000000
db_tda_pc_5.60.5_n4_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n4_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_pca_5.60.5_nb_n4_3_fold<-(db_nb_fit_re - db_tda_pc_5.60.5_n4_nb_fit_re)
diff_drybean_tda_pca_5.60.5_nb_n4_3_fold
## Accuracy
## 1 -0.05515025
## 2 -0.07877564
## 3 -0.08042655
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nb.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n4_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n4_3_fold$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n4_3_fold$probRight
bst_dbf_db_tda_pca_5.60.5_nb.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nb.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nb.n4_3_fold
## $winLeft
## [1] 0.9913333
##
## $winRope
## [1] 0.008666667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nb.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nb.n4_3_fold
## $left
## [1] 0.9886323
##
## $rope
## [1] 0.004801299
##
## $right
## [1] 0.006566353
# Rope Plot
plot(rope(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold))
#bf_tda_pca_5.60.5_nb.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold)
## t = -8.7517, df = 2, p-value = 0.01281
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.10657855 -0.03632308
## sample estimates:
## mean of x
## -0.07145081
### Test set diff
diff_drybean_tda_pca_5.60.5_nb.n4_test<-(db_nb_cf_ov_acc - db_tda_pc_5.60.5_n4_db_nb_cf0_ov_acc)
diff_drybean_tda_pca_5.60.5_nb.n4_test
## Accuracy
## 0.5470588
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n4_test),-0.01,0.01)
bst_dbf_db_tda_pca_5.60.5_nb.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_dbf_db_tda_pca_5.60.5_nb.n4_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n4_test$probLeft/bst_dbf_db_tda_pca_5.60.5_nb.n4_test$probRight
bst_dbf_db_tda_pca_5.60.5_nb.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_dbf_db_tda_pca_5.60.5_nb.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n4_test),-0.01,0.01)
bsr_dbf_db_tda_pca_5.60.5_nb.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1616
##
## $winRight
## [1] 0.8384
# Bayesian Correlated Test
bct_dbf_db_tda_pca_5.60.5_nb.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n4_test),0.1,-0.01,0.01)
bct_dbf_db_tda_pca_5.60.5_nb.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb.n4_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb.n4_test)) #bf_tda_pca_5.60.5_nb.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n4_test))
##Node5
#DryBean_TDA_PC_5.60.5_n5_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n5.vec,
# method = 'nb',
# trControl = fitControl,
# metric='Accuracy')
#DryBean_TDA_PC_5.60.5_n5_NbFit0
#DryBean_TDA_PC_5.60.5_n5_NbFit0$resample
#db_tda_pc_5.60.5_n5_nb_fit_re<-DryBean_TDA_PC_5.60.5_n5_NbFit0$resample[1]
#summary(DryBean_TDA_PC_5.60.5_n5_NbFit0)
# Predict outcome using DryBean_TDA_PC_5.60.5_n5_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_PC_5.60.5_n5_NbFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#db_tda_pc_5.60.5_n5_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#db_tda_pc_5.60.5_n5_db_nb_cf0
#db_tda_pc_5.60.5_n5_db_nb_cf0
#db_tda_pc_5.60.5_n5_db_nb_cf0$overall
#db_tda_pc_5.60.5_n5_db_nb_cf0_ov_acc<-db_tda_pc_5.60.5_n5_db_nb_cf0$overall[1]
#db_tda_pc_5.60.5_n5_db_nb_cf0$byClass
#db_tda_pc_5.60.5_n5_db_nb_cf0_pre_rec_f1<-db_tda_pc_5.60.5_n5_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_pca_5.60.5_nb_n5_3_fold<-(db_nb_fit_re - db_tda_pc_5.60.5_n5_nb_fit_re)
#diff_drybean_tda_pca_5.60.5_nb_n5_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n5_3_fold<-#BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n5_3_fold),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_nb.n5_3_fold
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n5_3_fold_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n5_3_fold$probLeft/#bst_dbf_db_tda_pca_5.60.5_nb.n5_3_fold$probRight
#bst_dbf_db_tda_pca_5.60.5_nb.n5_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_nb.n5_3_fold<-#BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n4_3_fold),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_nb.n5_3_fold
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_nb.n5_3_fold<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n5_3_fold),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_nb.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb_n5_3_fold))
#bf_tda_pca_5.60.5_nb.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb_n5_3_fold))
### Test set diff
#diff_drybean_tda_pca_5.60.5_nb.n5_test<-(db_nb_cf_ov_acc - db_tda_pc_5.60.5_n5_db_nb_cf0_ov_acc)
#diff_drybean_tda_pca_5.60.5_nb.n5_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n5_test<-#BayesianSignTest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n5_test),-0.01,0.01)
#bst_dbf_db_tda_pca_5.60.5_nb.n5_test
# Odds Left Bayesian Sign Test
#bst_dbf_db_tda_pca_5.60.5_nb.n5_test_odds.left<-bst_dbf_db_tda_pca_5.60.5_nb.n5_test$probLeft/#bst_dbf_db_tda_pca_5.60.5_nb.n5_test$probRight
#bst_dbf_db_tda_pca_5.60.5_nb.n5_test_odds.left
# Bayesian Signed Rank Test
#bsr_dbf_db_tda_pca_5.60.5_nb.n5_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n5_test),-0.01,0.01)
#bsr_dbf_db_tda_pca_5.60.5_nb.n5_test
# Bayesian Correlated Test
#bct_dbf_db_tda_pca_5.60.5_nb.n5_test<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n5_test),0.1,-0.01,0.01)
#bct_dbf_db_tda_pca_5.60.5_nb.n5_test
# Rope Plot
#plot(rope(diff_drybean_tda_pca_5.60.5_nb.n5_test)))
#BayesFactor
#bf_tda_pca_5.60.5_nb.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_pca_5.60.5_nb.n5_test)) #bf_tda_pca_5.60.5_nb.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_pca_5.60.5_nb.n5_test))
##With TDA KDE filter 5 intervals, 50% overlap, 5 bins
##Node1
DryBean_TDA_KDE_5.60.5_n1_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_kde_dry_bean_dataset_5.60.5.n1.vec,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
DryBean_TDA_KDE_5.60.5_n1_NbFit0
## Naive Bayes
##
## 7503 samples
## 16 predictor
## 7 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5001, 5004, 5001
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.9161661 0.8992656
## TRUE 0.9148348 0.8976668
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
## = 1.
DryBean_TDA_KDE_5.60.5_n1_NbFit0$resample
## Accuracy Kappa Resample
## 1 0.9228617 0.9072658 Fold1
## 2 0.9143657 0.8970742 Fold2
## 3 0.9112710 0.8934567 Fold3
nb_tda_kde_5.60.5_n1_nb_fit_re<-DryBean_TDA_KDE_5.60.5_n1_NbFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n1_NbFit0)
## Length Class Mode
## apriori 7 table numeric
## tables 16 -none- list
## levels 7 -none- character
## call 5 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 7 -none- character
## param 0 -none- list
#Predict outcome using DryBean_TDA_KDE_5.60.5_n1_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n1_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4079
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n1_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
nb_tda_kde_5.60.5_n1_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 282 0 30 0 1 3 0
## BOMBAY 1 156 1 0 0 0 0
## CALI 66 0 438 0 10 0 0
## DERMASON 0 0 0 768 3 1 14
## HOROZ 4 0 9 1 552 1 5
## SEKER 7 0 1 204 0 588 93
## SIRA 36 0 10 90 12 15 678
##
## Overall Statistics
##
## Accuracy : 0.8485
## 95% CI : (0.8372, 0.8594)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8182
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.71212 1.00000 0.8957 0.7225
## Specificity 0.99077 0.99949 0.9788 0.9940
## Pos Pred Value 0.89241 0.98734 0.8521 0.9771
## Neg Pred Value 0.96971 1.00000 0.9857 0.9104
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.06912 0.03824 0.1074 0.1882
## Detection Prevalence 0.07745 0.03873 0.1260 0.1926
## Balanced Accuracy 0.85145 0.99975 0.9373 0.8583
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9550 0.9671 0.8582
## Specificity 0.9943 0.9122 0.9505
## Pos Pred Value 0.9650 0.6585 0.8062
## Neg Pred Value 0.9926 0.9937 0.9654
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1353 0.1441 0.1662
## Detection Prevalence 0.1402 0.2189 0.2061
## Balanced Accuracy 0.9747 0.9396 0.9043
nb_tda_kde_5.60.5_n1_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 282 0 30 0 1 3 0
## BOMBAY 1 156 1 0 0 0 0
## CALI 66 0 438 0 10 0 0
## DERMASON 0 0 0 768 3 1 14
## HOROZ 4 0 9 1 552 1 5
## SEKER 7 0 1 204 0 588 93
## SIRA 36 0 10 90 12 15 678
##
## Overall Statistics
##
## Accuracy : 0.8485
## 95% CI : (0.8372, 0.8594)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.8182
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.71212 1.00000 0.8957 0.7225
## Specificity 0.99077 0.99949 0.9788 0.9940
## Pos Pred Value 0.89241 0.98734 0.8521 0.9771
## Neg Pred Value 0.96971 1.00000 0.9857 0.9104
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.06912 0.03824 0.1074 0.1882
## Detection Prevalence 0.07745 0.03873 0.1260 0.1926
## Balanced Accuracy 0.85145 0.99975 0.9373 0.8583
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9550 0.9671 0.8582
## Specificity 0.9943 0.9122 0.9505
## Pos Pred Value 0.9650 0.6585 0.8062
## Neg Pred Value 0.9926 0.9937 0.9654
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1353 0.1441 0.1662
## Detection Prevalence 0.1402 0.2189 0.2061
## Balanced Accuracy 0.9747 0.9396 0.9043
nb_tda_kde_5.60.5_n1_db_nb_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8485294 0.8182327 0.8371572 0.8594001 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n1_db_nb_cf0_ov_acc<-nb_tda_kde_5.60.5_n1_db_nb_cf0$overall[1]
nb_tda_kde_5.60.5_n1_db_nb_cf0$byClas1
## NULL
nb_tda_kde_5.60.5_n1_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n1_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nb_n1_3_fold<-(db_nb_fit_re - nb_tda_kde_5.60.5_n1_nb_fit_re)
diff_drybean_tda_kde_5.60.5_nb_n1_3_fold
## Accuracy
## 1 -0.014801257
## 2 -0.016631322
## 3 -0.008532551
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n1_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nb.n1_3_fold
## $probLeft
## [1] 0.5
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n1_3_fold_odds.left<-bst_tda_kde_5.60.5_nb.n1_3_fold$probLeft/bst_tda_kde_5.60.5_nb.n1_3_fold$probRight
bst_tda_kde_5.60.5_nb.n1_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nb.n1_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nb.n1_3_fold
## $winLeft
## [1] 0.6965
##
## $winRope
## [1] 0.3035
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nb.n1_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nb.n1_3_fold
## $left
## [1] 0.8192258
##
## $rope
## [1] 0.173563
##
## $right
## [1] 0.007211266
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n1_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold))
#bf_tda_kde_5.60.5_nb.n1_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nb_n1_3_fold)
## t = -5.4326, df = 2, p-value = 0.03225
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.023872519 -0.002770901
## sample estimates:
## mean of x
## -0.01332171
### Test set diff
diff_drybean_tda_kde_5.60.5_nb.n1_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n1_db_nb_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nb.n1_test
## Accuracy
## 0.07892157
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n1_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n1_test),-0.01,0.01)
bst_tda_kde_5.60.5_nb.n1_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n1_test_odds.left<-bst_tda_kde_5.60.5_nb.n1_test$probLeft/bst_tda_kde_5.60.5_nb.n1_test$probRight
bst_tda_kde_5.60.5_nb.n1_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nb.n1_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n1_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nb.n1_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1610667
##
## $winRight
## [1] 0.8389333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nb.n1_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n1_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nb.n1_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb.n1_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n1_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb.n1_test)) #bf_tda_pca_5.60.5_nb.n1_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n1_test))
##Node2
DryBean_TDA_KDE_5.60.5_n2_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n2.vec,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
DryBean_TDA_KDE_5.60.5_n2_NbFit0
## Naive Bayes
##
## 8024 samples
## 16 predictor
## 6 classes: 'BARBUNYA', 'CALI', 'DERMASON', 'HOROZ', 'SEKER', 'SIRA'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 5349, 5349, 5350
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.8535645 0.8111121
## TRUE 0.8585504 0.8171406
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = TRUE and adjust
## = 1.
DryBean_TDA_KDE_5.60.5_n2_NbFit0$resample
## Accuracy Kappa Resample
## 1 0.8628037 0.8226321 Fold1
## 2 0.8456075 0.8003390 Fold2
## 3 0.8672401 0.8284508 Fold3
nb_tda_kde_5.60.5_n2_nb_fit_re<-DryBean_TDA_KDE_5.60.5_n2_NbFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n2_NbFit0)
## Length Class Mode
## apriori 6 table numeric
## tables 16 -none- list
## levels 6 -none- character
## call 6 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 6 -none- character
## param 0 -none- list
# Predict outcome using DryBean_TDA_KDE_5.60.5_n2_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n2_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n2_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n2_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 300 2 67 0 3 2 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 68 80 412 0 14 0 6
## DERMASON 0 64 0 985 5 52 89
## HOROZ 4 3 7 1 551 0 19
## SEKER 5 7 1 21 0 541 28
## SIRA 19 0 2 56 5 13 645
##
## Overall Statistics
##
## Accuracy : 0.8417
## 95% CI : (0.8301, 0.8527)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.807
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.75758 0.00000 0.8425 0.9266
## Specificity 0.97910 1.00000 0.9532 0.9304
## Pos Pred Value 0.79576 NaN 0.7103 0.8243
## Neg Pred Value 0.97408 0.96176 0.9780 0.9730
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07353 0.00000 0.1010 0.2414
## Detection Prevalence 0.09240 0.00000 0.1422 0.2929
## Balanced Accuracy 0.86834 0.50000 0.8979 0.9285
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9533 0.8898 0.8165
## Specificity 0.9903 0.9821 0.9711
## Pos Pred Value 0.9419 0.8972 0.8716
## Neg Pred Value 0.9923 0.9807 0.9566
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1350 0.1326 0.1581
## Detection Prevalence 0.1434 0.1478 0.1814
## Balanced Accuracy 0.9718 0.9360 0.8938
nb_tda_kde_5.60.5_n2_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 300 2 67 0 3 2 3
## BOMBAY 0 0 0 0 0 0 0
## CALI 68 80 412 0 14 0 6
## DERMASON 0 64 0 985 5 52 89
## HOROZ 4 3 7 1 551 0 19
## SEKER 5 7 1 21 0 541 28
## SIRA 19 0 2 56 5 13 645
##
## Overall Statistics
##
## Accuracy : 0.8417
## 95% CI : (0.8301, 0.8527)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.807
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.75758 0.00000 0.8425 0.9266
## Specificity 0.97910 1.00000 0.9532 0.9304
## Pos Pred Value 0.79576 NaN 0.7103 0.8243
## Neg Pred Value 0.97408 0.96176 0.9780 0.9730
## Prevalence 0.09706 0.03824 0.1199 0.2605
## Detection Rate 0.07353 0.00000 0.1010 0.2414
## Detection Prevalence 0.09240 0.00000 0.1422 0.2929
## Balanced Accuracy 0.86834 0.50000 0.8979 0.9285
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9533 0.8898 0.8165
## Specificity 0.9903 0.9821 0.9711
## Pos Pred Value 0.9419 0.8972 0.8716
## Neg Pred Value 0.9923 0.9807 0.9566
## Prevalence 0.1417 0.1490 0.1936
## Detection Rate 0.1350 0.1326 0.1581
## Detection Prevalence 0.1434 0.1478 0.1814
## Balanced Accuracy 0.9718 0.9360 0.8938
nb_tda_kde_5.60.5_n2_db_nb_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8416667 0.8069643 0.8300987 0.8527430 0.2605392
## AccuracyPValue McnemarPValue
## 0.0000000 NaN
nb_tda_kde_5.60.5_n2_db_nb_cf0_ov_acc<-nb_tda_kde_5.60.5_n2_db_nb_cf0$overall[1]
nb_tda_kde_5.60.5_n2_db_nb_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.7575758 0.9790988 0.7957560 0.9740751 0.7957560
## Class: BOMBAY 0.0000000 1.0000000 NaN 0.9617647 NA
## Class: CALI 0.8425358 0.9532164 0.7103448 0.9780000 0.7103448
## Class: DERMASON 0.9266228 0.9303944 0.8242678 0.9729636 0.8242678
## Class: HOROZ 0.9532872 0.9902913 0.9418803 0.9922747 0.9418803
## Class: SEKER 0.8898026 0.9821429 0.8971808 0.9807305 0.8971808
## Class: SIRA 0.8164557 0.9711246 0.8716216 0.9565868 0.8716216
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.7575758 0.7761966 0.09705882 0.07352941
## Class: BOMBAY 0.0000000 NA 0.03823529 0.00000000
## Class: CALI 0.8425358 0.7708138 0.11985294 0.10098039
## Class: DERMASON 0.9266228 0.8724535 0.26053922 0.24142157
## Class: HOROZ 0.9532872 0.9475494 0.14166667 0.13504902
## Class: SEKER 0.8898026 0.8934765 0.14901961 0.13259804
## Class: SIRA 0.8164557 0.8431373 0.19362745 0.15808824
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.09240196 0.8683373
## Class: BOMBAY 0.00000000 0.5000000
## Class: CALI 0.14215686 0.8978761
## Class: DERMASON 0.29289216 0.9285086
## Class: HOROZ 0.14338235 0.9717892
## Class: SEKER 0.14779412 0.9359727
## Class: SIRA 0.18137255 0.8937902
nb_tda_kde_5.60.5_n2_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n2_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nb_n2_3_fold<-(db_nb_fit_re - nb_tda_kde_5.60.5_n2_nb_fit_re)
diff_drybean_tda_kde_5.60.5_nb_n2_3_fold
## Accuracy
## 1 0.04525672
## 2 0.05212695
## 3 0.03549834
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n2_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nb.n2_3_fold
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0.75
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n2_3_fold_odds.left<-bst_tda_kde_5.60.5_nb.n2_3_fold$probLeft/bst_tda_kde_5.60.5_nb.n2_3_fold$probRight
bst_tda_kde_5.60.5_nb.n2_3_fold_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nb.n2_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nb.n2_3_fold
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.008766667
##
## $winRight
## [1] 0.9912333
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nb.n2_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nb.n2_3_fold
## $left
## [1] 0.005181889
##
## $rope
## [1] 0.007511021
##
## $right
## [1] 0.9873071
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n2_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold))
#bf_tda_kde_5.60.5_nb.n2_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nb_n2_3_fold)
## t = 9.1814, df = 2, p-value = 0.01166
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.02353655 0.06505146
## sample estimates:
## mean of x
## 0.044294
### Test set diff
diff_drybean_tda_kde_5.60.5_nb.n2_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n2_db_nb_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nb.n2_test
## Accuracy
## 0.08578431
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n2_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n2_test),-0.01,0.01)
bst_tda_kde_5.60.5_nb.n2_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n2_test_odds.left<-bst_tda_kde_5.60.5_nb.n2_test$probLeft/bst_tda_kde_5.60.5_nb.n2_test$probRight
bst_tda_kde_5.60.5_nb.n2_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nb.n2_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n2_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nb.n2_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1609
##
## $winRight
## [1] 0.8391
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nb.n2_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n2_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nb.n2_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb.n2_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n2_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb.n2_test)) #bf_tda_kde_5.60.5_nb.n2_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n2_test))
##Node3
#DryBean_TDA_KDE_5.60.5_n3_NbFit0 <- train(as.factor(Class) ~ ., data = #tda.m_dry_bean_dataset_5.60.5.n3.vec,
# method = 'nb',
# trControl = fitControl,
# metric='Accuracy')
#DryBean_TDA_KDE_5.60.5_n3_NbFit0
#DryBean_TDA_KDE_5.60.5_n3_NbFit0$resample
#nb_tda_kde_5.60.5_n3_nb_fit_re<-DryBean_TDA_KDE_5.60.5_n3_NbFit0$resample[1]
#summary(DryBean_TDA_KDE_5.60.5_n3_NbFit0)
#Predict outcome using DryBean_TDA_KDE_5.60.5_n3_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_KDE_5.60.5_n3_NbFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#nb_tda_kde_5.60.5_n3_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#nb_tda_kde_5.60.5_n3_db_nb_cf0
#nb_tda_kde_5.60.5_n3_db_nb_cf0
#nb_tda_kde_5.60.5_n3_db_nb_cf0$overall
#nb_tda_kde_5.60.5_n3_db_nb_cf0_ov_acc<-nb_tda_kde_5.60.5_n3_db_nb_cf0$overall[1]
#nb_tda_kde_5.60.5_n3_db_nb_cf0$byClass
#nb_tda_kde_5.60.5_n3_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n3_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_kde_5.60.5_nb_n3_3_fold<-(db_nb_fit_re - nb_tda_kde_5.60.5_n3_nb_fit_re)
#diff_drybean_tda_kde_5.60.5_nb_n3_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n3_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n3_3_fold),-0.01,0.01)
#bst_tda_kde_5.60.5_nb.n3_3_fold
# Odds Left Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n3_3_fold_odds.left<-bst_tda_kde_5.60.5_nb.n3_3_fold$probLeft/bst_tda_kde_5.60.5_nb.n3_3_fold$probRight
#bst_tda_kde_5.60.5_nb.n3_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_tda_kde_5.60.5_nb.n3_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n3_3_fold),-0.01,0.01)
#bsr_tda_kde_5.60.5_nb.n3_3_fold
# Bayesian Correlated Test
#bct_tda_kde_5.60.5_nb.n3_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n3_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.60.5_nb.n3_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb_n3_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n3_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb_n3_3_fold))
#bf_tda_kde_5.60.5_nb.n3_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n3_3_fold))
### Test set diff
#diff_drybean_tda_kde_5.60.5_nb.n3_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n3_db_nb_cf0_ov_acc)
#diff_drybean_tda_kde_5.60.5_nb.n3_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n3_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n3_test),-0.01,0.01)
#bst_tda_kde_5.60.5_nb.n3_test
# Odds Left Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n3_test_odds.left<-bst_tda_kde_5.60.5_nb.n3_test$probLeft/bst_tda_kde_5.60.5_nb.n3_test$probRight
#bst_tda_kde_5.60.5_nb.n3_test_odds.left
# Bayesian Signed Rank Test
#bsr_tda_kde_5.60.5_nb.n3_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n3_test),-0.01,0.01)
#bsr_tda_kde_5.60.5_nb.n3_test
# Bayesian Correlated Test
#bct_tda_kde_5.60.5_nb.n3_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n3_test),0.1,-0.01,0.01)
#bct_tda_kde_5.60.5_nb.n3_test
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb.n3_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n3_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb.n3_test)) #bf_tda_kde_5.60.5_nb.n3_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n3_test))
##Node4
DryBean_TDA_KDE_5.60.5_n4_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n4.vec,
method = 'nb',
trControl = fitControl,
metric='Accuracy')
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
DryBean_TDA_KDE_5.60.5_n4_NbFit0
## Naive Bayes
##
## 894 samples
## 16 predictor
## 4 classes: 'BARBUNYA', 'BOMBAY', 'CALI', 'HOROZ'
##
## No pre-processing
## Resampling: Cross-Validated (3 fold)
## Summary of sample sizes: 597, 594, 597
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa
## FALSE 0.9686756 0.9495951
## TRUE 0.9675533 0.9476681
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
## = 1.
DryBean_TDA_KDE_5.60.5_n4_NbFit0$resample
## Accuracy Kappa Resample
## 1 0.956229 0.9294408 Fold1
## 2 0.970000 0.9518717 Fold2
## 3 0.979798 0.9674728 Fold3
nb_tda_kde_5.60.5_n4_nb_fit_re<-DryBean_TDA_KDE_5.60.5_n4_NbFit0$resample[1]
summary(DryBean_TDA_KDE_5.60.5_n4_NbFit0)
## Length Class Mode
## apriori 4 table numeric
## tables 16 -none- list
## levels 4 -none- character
## call 5 -none- call
## x 16 data.frame list
## usekernel 1 -none- logical
## varnames 16 -none- character
## xNames 16 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 4 -none- character
## param 0 -none- list
# Predict outcome using DryBean_TDA_KDE_5.60.5_n4_NbFit0 from training data based on testing data
pred0 <- predict(DryBean_TDA_KDE_5.60.5_n4_NbFit0, newdata= Dry_Bean_DatasetTest)
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 78
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 79
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 80
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 81
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 82
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 83
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 84
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 85
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 86
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 87
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 88
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 89
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 90
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 91
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 92
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 93
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 94
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 95
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 96
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 97
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 98
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 99
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3080
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3081
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3082
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3083
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3084
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3085
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3086
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3087
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3088
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3089
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3090
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3091
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3092
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3093
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3094
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3095
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3096
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3097
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3098
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3099
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3100
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3101
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3102
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3103
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3104
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3105
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3106
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3107
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3108
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3109
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3110
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3111
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3112
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3113
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3114
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3115
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3116
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3117
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3118
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3119
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3120
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3121
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3122
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3123
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3124
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3125
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3126
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3127
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3128
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3129
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3130
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3131
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3132
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3133
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3134
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3135
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3136
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3137
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3138
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3139
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3140
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3141
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3142
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3143
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3144
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3145
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3146
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3147
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3148
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3149
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3150
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3151
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3152
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3153
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3154
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3155
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3156
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3157
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3158
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3159
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3160
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3161
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3162
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3163
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3164
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3165
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3166
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3167
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3168
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3169
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3170
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3171
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3172
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3173
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3174
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3175
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3176
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3177
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3178
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3179
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3180
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3181
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3182
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3183
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3184
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3185
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3186
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3187
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3188
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3189
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3190
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3191
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3192
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3193
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3194
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3195
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3196
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3197
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3198
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3199
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3200
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3201
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3202
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3203
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3204
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3205
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3206
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3207
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3208
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3209
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3210
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3211
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3212
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3213
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3214
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3215
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3216
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3217
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3218
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3219
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3220
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3221
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3222
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3223
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3224
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3225
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3226
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3227
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3228
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3229
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3230
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3231
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3232
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3233
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3234
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3235
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3236
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3237
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3238
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3239
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3240
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3241
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3242
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3243
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3244
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3245
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3246
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3247
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3248
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3249
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3250
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3251
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3252
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3253
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3254
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3255
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3256
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3257
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3258
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3259
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3260
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3261
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3262
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3263
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3264
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3265
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3266
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3267
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3268
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3269
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3270
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3271
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3272
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3273
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3274
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3275
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3276
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3277
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3278
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3279
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3280
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3281
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3282
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3283
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3284
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3285
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3286
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3287
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3288
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3289
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3290
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3291
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3292
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3293
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3294
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3295
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3296
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3297
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3298
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3299
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3300
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3301
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3302
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3303
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3304
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3305
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3306
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3307
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3308
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3309
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3310
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3311
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3312
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3313
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3314
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3315
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3316
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3317
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3318
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3319
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3320
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3321
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3322
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3323
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3324
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3325
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3326
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3327
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3328
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3329
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3330
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3331
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3332
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3333
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3334
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3335
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3336
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3337
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3338
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3339
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3340
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3341
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3342
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3343
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3344
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3345
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3346
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3347
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3348
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3349
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3350
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3351
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3352
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3353
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3354
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3355
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3356
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3357
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3358
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3359
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3360
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3361
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3362
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3363
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3364
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3365
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3366
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3367
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3368
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3369
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3370
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3371
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3372
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3373
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3374
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3375
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3376
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3377
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3378
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3379
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3380
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3381
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3382
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3383
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3384
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3385
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3386
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3387
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3388
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3389
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3390
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3391
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3392
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3393
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3394
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3395
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3396
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3397
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3398
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3399
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3400
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3401
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3402
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3403
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3404
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3405
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3406
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3407
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3408
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3409
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3410
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3411
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3412
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3413
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3414
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3415
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3416
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3417
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3418
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3419
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3420
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3421
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3422
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3423
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3424
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3425
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3426
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3427
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3428
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3429
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3430
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3431
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3432
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3433
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3434
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3435
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3436
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3437
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3438
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3439
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3440
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3441
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3442
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3443
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3444
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3445
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3446
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3447
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3448
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3449
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3450
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3451
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3452
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3453
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3454
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3455
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3456
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3457
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3458
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3459
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3460
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3461
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3462
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3463
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3464
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3465
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3466
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3467
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3468
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3469
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3470
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3471
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3472
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3473
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3474
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3475
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3476
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3477
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3478
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3479
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3480
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3481
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3482
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3483
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3484
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3485
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3486
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3487
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3488
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3489
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3490
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3491
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3492
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3493
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3494
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3495
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3496
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3497
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3498
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3499
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3500
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3501
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3502
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3503
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3504
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3505
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3506
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3507
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3508
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3509
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3510
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3511
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3512
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3513
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3514
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3515
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3516
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3517
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3518
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3519
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3520
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3521
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3522
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3523
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3524
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3525
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3526
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3527
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3528
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3529
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3530
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3531
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3532
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3533
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3534
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3535
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3536
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3537
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3538
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3539
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3540
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3541
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3542
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3543
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3544
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3545
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3546
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3547
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3548
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3549
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3550
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3551
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3552
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3553
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3554
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3555
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3556
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3557
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3558
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3559
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3560
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3561
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3562
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3563
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3564
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3565
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3566
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3567
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3568
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3569
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3570
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3571
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3572
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3573
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3574
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3575
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3576
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3577
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3578
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3579
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3580
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3581
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3582
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3583
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3584
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3585
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3586
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3587
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3588
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3589
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3590
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3591
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3592
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3593
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3594
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3595
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3596
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3597
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3598
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3599
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3600
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3601
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3602
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3603
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3604
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3605
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3606
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3607
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3608
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3609
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3610
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3611
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3612
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3613
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3614
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3615
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3616
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3617
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3618
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3619
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3620
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3621
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3622
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3623
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3624
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3625
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3626
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3627
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3628
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3629
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3630
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3631
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3632
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3633
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3634
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3635
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3636
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3637
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3638
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3639
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3640
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3641
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3642
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3643
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3644
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3645
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3646
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3647
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3648
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3649
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3650
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3651
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3652
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3653
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3654
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3655
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3656
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3657
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3658
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3659
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3660
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3661
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3662
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3663
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3664
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3665
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3666
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3667
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3668
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3669
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3670
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3671
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3672
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3673
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3674
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3675
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3676
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3677
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3678
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3679
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3680
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3681
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3682
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3683
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3684
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3685
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3686
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3687
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3688
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3689
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3690
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3691
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3692
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3693
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3694
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3695
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3696
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3697
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3698
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3699
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3700
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3701
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3702
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3703
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3704
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3705
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3706
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3707
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3708
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3709
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3710
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3711
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3712
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3713
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3714
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3715
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3716
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3717
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3718
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3719
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3720
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3721
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3722
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3723
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3724
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3725
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3726
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3727
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3728
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3729
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3730
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3731
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3732
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3733
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3734
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3735
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3736
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3737
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3738
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3739
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3740
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3741
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3742
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3743
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3744
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3745
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3746
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3747
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3748
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3749
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3750
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3751
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3752
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3753
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3754
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3755
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3756
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3757
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3758
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3759
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3760
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3761
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3762
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3763
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3764
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3765
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3766
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3767
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3768
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3769
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3770
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3771
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3772
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3773
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3774
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3775
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3776
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3777
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3778
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3779
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3780
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3781
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3782
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3783
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3784
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3785
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3786
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3787
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3788
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3789
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3790
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3791
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3792
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3793
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3794
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3795
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3796
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3797
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3798
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3799
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3800
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3801
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3802
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3803
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3804
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3805
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3806
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3807
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3808
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3809
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3810
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3811
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3812
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3813
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3814
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3815
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3816
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3817
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3818
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3819
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3820
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3821
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3822
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3823
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3824
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3825
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3826
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3827
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3828
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3829
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3830
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3831
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3832
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3833
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3834
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3835
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3836
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3837
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3838
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3839
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3840
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3841
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3842
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3843
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3844
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3845
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3846
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3847
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3848
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3849
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3850
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3851
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3852
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3853
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3854
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3855
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3856
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3857
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3858
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3859
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3860
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3861
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3862
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3863
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3864
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3865
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3866
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3867
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3868
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3869
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3870
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3871
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3872
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3873
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3874
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3875
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3876
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3877
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3878
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3879
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3880
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3881
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3882
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3883
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3884
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3885
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3886
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3887
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3888
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3889
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3890
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3891
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3892
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3893
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3894
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3895
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3896
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3897
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3898
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3899
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3900
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3901
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3902
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3903
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3904
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3905
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3906
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3907
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3908
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3909
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3910
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3911
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3912
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3913
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3914
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3915
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3916
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3917
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3918
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3919
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3920
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3921
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3922
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3923
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3924
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3925
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3926
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3927
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3928
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3929
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3930
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3931
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3932
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3933
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3934
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3935
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3936
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3937
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3938
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3939
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3940
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3941
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3942
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3943
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3944
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3945
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3946
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3947
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3948
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3949
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3950
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3951
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3952
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3953
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3954
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3955
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3956
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3957
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3958
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3959
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3960
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3961
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3962
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3963
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3964
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3965
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3966
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3967
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3968
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3969
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3970
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3971
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3972
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3973
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3974
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3975
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3976
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3977
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3978
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3979
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3980
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3981
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3982
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3983
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3984
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3985
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3986
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3987
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3988
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3989
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3990
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3991
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3992
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3993
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3994
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3995
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3996
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3997
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3998
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3999
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4000
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4001
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4002
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4003
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4004
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4005
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4006
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4007
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4008
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4009
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4010
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4011
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4012
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4013
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4014
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4015
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4016
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4017
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4018
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4019
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4020
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4021
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4022
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4023
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4024
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4025
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4026
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4027
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4028
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4029
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4030
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4031
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4032
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4033
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4034
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4035
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4036
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4037
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4038
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4039
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4040
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4041
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4042
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4043
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4044
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4045
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4046
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4047
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4048
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4049
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4050
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4051
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4052
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4053
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4054
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4055
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4056
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4057
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4058
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4059
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4060
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4061
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4062
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4063
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4064
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4065
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4066
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4067
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4068
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4069
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4070
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4071
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4072
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4073
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4074
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4075
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4076
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4077
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4078
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4079
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4080
# Create confusion matrix to assess model fit/performance on test data
nb_tda_kde_5.60.5_n4_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
## Warning in levels(reference) != levels(data): longer object length is not a
## multiple of shorter object length
## Warning in confusionMatrix.default(data = pred0,
## as.factor(Dry_Bean_DatasetTest$Class)): Levels are not in the same order for
## reference and data. Refactoring data to match.
nb_tda_kde_5.60.5_n4_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 333 0 76 39 1 586 44
## BOMBAY 1 156 0 0 0 0 0
## CALI 25 0 354 0 3 0 0
## DERMASON 0 0 0 0 0 0 0
## HOROZ 37 0 59 1024 574 22 746
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3473
## 95% CI : (0.3327, 0.3621)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.255
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.84091 1.00000 0.72393 0.0000
## Specificity 0.79750 0.99975 0.99220 1.0000
## Pos Pred Value 0.30862 0.99363 0.92670 NaN
## Neg Pred Value 0.97901 1.00000 0.96349 0.7395
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.08162 0.03824 0.08676 0.0000
## Detection Prevalence 0.26446 0.03848 0.09363 0.0000
## Balanced Accuracy 0.81921 0.99987 0.85806 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9931 0.000 0.0000
## Specificity 0.4609 1.000 1.0000
## Pos Pred Value 0.2331 NaN NaN
## Neg Pred Value 0.9975 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1407 0.000 0.0000
## Detection Prevalence 0.6034 0.000 0.0000
## Balanced Accuracy 0.7270 0.500 0.5000
nb_tda_kde_5.60.5_n4_db_nb_cf0
## Confusion Matrix and Statistics
##
## Reference
## Prediction BARBUNYA BOMBAY CALI DERMASON HOROZ SEKER SIRA
## BARBUNYA 333 0 76 39 1 586 44
## BOMBAY 1 156 0 0 0 0 0
## CALI 25 0 354 0 3 0 0
## DERMASON 0 0 0 0 0 0 0
## HOROZ 37 0 59 1024 574 22 746
## SEKER 0 0 0 0 0 0 0
## SIRA 0 0 0 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3473
## 95% CI : (0.3327, 0.3621)
## No Information Rate : 0.2605
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.255
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: BARBUNYA Class: BOMBAY Class: CALI Class: DERMASON
## Sensitivity 0.84091 1.00000 0.72393 0.0000
## Specificity 0.79750 0.99975 0.99220 1.0000
## Pos Pred Value 0.30862 0.99363 0.92670 NaN
## Neg Pred Value 0.97901 1.00000 0.96349 0.7395
## Prevalence 0.09706 0.03824 0.11985 0.2605
## Detection Rate 0.08162 0.03824 0.08676 0.0000
## Detection Prevalence 0.26446 0.03848 0.09363 0.0000
## Balanced Accuracy 0.81921 0.99987 0.85806 0.5000
## Class: HOROZ Class: SEKER Class: SIRA
## Sensitivity 0.9931 0.000 0.0000
## Specificity 0.4609 1.000 1.0000
## Pos Pred Value 0.2331 NaN NaN
## Neg Pred Value 0.9975 0.851 0.8064
## Prevalence 0.1417 0.149 0.1936
## Detection Rate 0.1407 0.000 0.0000
## Detection Prevalence 0.6034 0.000 0.0000
## Balanced Accuracy 0.7270 0.500 0.5000
nb_tda_kde_5.60.5_n4_db_nb_cf0$overall
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 3.473039e-01 2.550431e-01 3.326858e-01 3.621410e-01 2.605392e-01
## AccuracyPValue McnemarPValue
## 1.093830e-34 NaN
nb_tda_kde_5.60.5_n4_db_nb_cf0_ov_acc<-nb_tda_kde_5.60.5_n4_db_nb_cf0$overall[1]
nb_tda_kde_5.60.5_n4_db_nb_cf0$byClass
## Sensitivity Specificity Pos Pred Value Neg Pred Value Precision
## Class: BARBUNYA 0.8409091 0.7975027 0.3086191 0.9790070 0.3086191
## Class: BOMBAY 1.0000000 0.9997452 0.9936306 1.0000000 0.9936306
## Class: CALI 0.7239264 0.9922027 0.9267016 0.9634938 0.9267016
## Class: DERMASON 0.0000000 1.0000000 NaN 0.7394608 NA
## Class: HOROZ 0.9930796 0.4608795 0.2331438 0.9975278 0.2331438
## Class: SEKER 0.0000000 1.0000000 NaN 0.8509804 NA
## Class: SIRA 0.0000000 1.0000000 NaN 0.8063725 NA
## Recall F1 Prevalence Detection Rate
## Class: BARBUNYA 0.8409091 0.4515254 0.09705882 0.08161765
## Class: BOMBAY 1.0000000 0.9968051 0.03823529 0.03823529
## Class: CALI 0.7239264 0.8128588 0.11985294 0.08676471
## Class: DERMASON 0.0000000 NA 0.26053922 0.00000000
## Class: HOROZ 0.9930796 0.3776316 0.14166667 0.14068627
## Class: SEKER 0.0000000 NA 0.14901961 0.00000000
## Class: SIRA 0.0000000 NA 0.19362745 0.00000000
## Detection Prevalence Balanced Accuracy
## Class: BARBUNYA 0.26446078 0.8192059
## Class: BOMBAY 0.03848039 0.9998726
## Class: CALI 0.09362745 0.8580646
## Class: DERMASON 0.00000000 0.5000000
## Class: HOROZ 0.60343137 0.7269795
## Class: SEKER 0.00000000 0.5000000
## Class: SIRA 0.00000000 0.5000000
nb_tda_kde_5.60.5_n4_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n4_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
diff_drybean_tda_kde_5.60.5_nb_n4_3_fold<-(db_nb_fit_re - nb_tda_kde_5.60.5_n4_nb_fit_re)
diff_drybean_tda_kde_5.60.5_nb_n4_3_fold
## Accuracy
## 1 -0.04816850
## 2 -0.07226558
## 3 -0.07705955
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n4_3_fold<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold),-0.01,0.01)
bst_tda_kde_5.60.5_nb.n4_3_fold
## $probLeft
## [1] 0.75
##
## $probRope
## [1] 0.25
##
## $probRight
## [1] 0
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n4_3_fold_odds.left<-bst_tda_kde_5.60.5_nb.n4_3_fold$probLeft/bst_tda_kde_5.60.5_nb.n4_3_fold$probRight
bst_tda_kde_5.60.5_nb.n4_3_fold_odds.left
## [1] Inf
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nb.n4_3_fold<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold),-0.01,0.01)
bsr_tda_kde_5.60.5_nb.n4_3_fold
## $winLeft
## [1] 0.9910333
##
## $winRope
## [1] 0.008966667
##
## $winRight
## [1] 0
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nb.n4_3_fold<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nb.n4_3_fold
## $left
## [1] 0.983739
##
## $rope
## [1] 0.007246671
##
## $right
## [1] 0.009014324
# Rope Plot
plot(rope(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n4_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold))
#bf_tda_kde_5.60.5_nb.n4_3_fold
#t_test
t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold))
##
## One Sample t-test
##
## data: as.matrix(diff_drybean_tda_kde_5.60.5_nb_n4_3_fold)
## t = -7.3644, df = 2, p-value = 0.01794
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.10429316 -0.02736925
## sample estimates:
## mean of x
## -0.06583121
### Test set diff
diff_drybean_tda_kde_5.60.5_nb.n4_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n4_db_nb_cf0_ov_acc)
diff_drybean_tda_kde_5.60.5_nb.n4_test
## Accuracy
## 0.5801471
## Bayesian Tests Test set diff
# Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n4_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n4_test),-0.01,0.01)
bst_tda_kde_5.60.5_nb.n4_test
## $probLeft
## [1] 0
##
## $probRope
## [1] 0.5
##
## $probRight
## [1] 0.5
# Odds Left Bayesian Sign Test
bst_tda_kde_5.60.5_nb.n4_test_odds.left<-bst_tda_kde_5.60.5_nb.n4_test$probLeft/bst_tda_kde_5.60.5_nb.n4_test$probRight
bst_tda_kde_5.60.5_nb.n4_test_odds.left
## [1] 0
# Bayesian Signed Rank Test
bsr_tda_kde_5.60.5_nb.n4_test<-BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n4_test),-0.01,0.01)
bsr_tda_kde_5.60.5_nb.n4_test
## $winLeft
## [1] 0
##
## $winRope
## [1] 0.1629333
##
## $winRight
## [1] 0.8370667
# Bayesian Correlated Test
bct_tda_kde_5.60.5_nb.n4_test<-correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n4_test),0.1,-0.01,0.01)
bct_tda_kde_5.60.5_nb.n4_test
## $left
## [1] NA
##
## $rope
## [1] NA
##
## $right
## [1] NA
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb.n4_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n4_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb.n4_test)) #bf_tda_kde_5.60.5_nb.n4_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n4_test))
##Node5
#DryBean_TDA_KDE_5.60.5_n5_NbFit0 <- train(as.factor(Class) ~ ., data = tda.m_dry_bean_dataset_5.60.5.n5.vec,
# method = 'nb',
# trControl = fitControl,
# metric='Accuracy')
#DryBean_TDA_KDE_5.60.5_n5_NbFit0
#DryBean_TDA_KDE_5.60.5_n5_NbFit0$resample
#nb_tda_kde_5.60.5_n5_nb_fit_re<-DryBean_TDA_KDE_5.60.5_n5_NbFit0$resample[1]
#summary(DryBean_TDA_KDE_5.60.5_n5_NbFit0)
# Predict outcome using DryBean_TDA_KDE_5.60.5_n5_NbFit0 from training data based on testing data
#pred0 <- predict(DryBean_TDA_KDE_5.60.5_n5_NbFit0, newdata= Dry_Bean_DatasetTest)
# Create confusion matrix to assess model fit/performance on test data
#nb_tda_kde_5.60.5_n5_db_nb_cf0<-confusionMatrix(data=pred0, as.factor(Dry_Bean_DatasetTest$Class))
#nb_tda_kde_5.60.5_n5_db_nb_cf0
#nb_tda_kde_5.60.5_n5_db_nb_cf0
#nb_tda_kde_5.60.5_n5_db_nb_cf0$overall
#nb_tda_kde_5.60.5_n5_db_nb_cf0_ov_acc<-nb_tda_kde_5.60.5_n5_db_nb_cf0$overall[1]
#nb_tda_kde_5.60.5_n5_db_nb_cf0$byClass
#nb_tda_kde_5.60.5_n5_db_nb_cf0_pre_rec_f1<-nb_tda_kde_5.60.5_n5_db_nb_cf0$byClass[5:7]
###### Conduct initial Bayesian tests of non-tda-assisted RF vs. tda-assisted RF classifiers
### 3-fold diff
#diff_drybean_tda_kde_5.60.5_nb_n5_3_fold<-(db_nb_fit_re - nb_tda_kde_5.60.5_n5_nb_fit_re)
#diff_drybean_tda_kde_5.60.5_nb_n5_3_fold
## Bayesian Tests 3-fold diff
# Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n5_3_fold<-#BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n5_3_fold),-0.01,0.01)
#bst_tda_kde_5.60.5_nb.n5_3_fold
# Odds Left Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n5_3_fold_odds.left<-bst_tda_kde_5.60.5_nb.n5_3_fold$probLeft/#bst_tda_kde_5.60.5_nb.n5_3_fold$probRight
#bst_tda_kde_5.60.5_nb.n5_3_fold_odds.left
# Bayesian Signed Rank Test
#bsr_tda_kde_5.60.5_nb.n5_3_fold<-#BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n5_3_fold),-0.01,0.01)
#bsr_tda_kde_5.60.5_nb.n5_3_fold
# Bayesian Correlated Test
#bct_tda_kde_5.60.5_nb.n5_3_fold<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n5_3_fold),0.1,-0.01,0.01)
#bct_tda_kde_5.60.5_nb.n5_3_fold
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb_n5_3_fold,c(-0.01,0.01)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n5_3_fold = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb_n5_3_fold))
#bf_tda_kde_5.60.5_nb.n5_3_fold
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb_n5_3_fold))
### Test set diff
#diff_drybean_tda_kde_5.60.5_nb.n5_test<-(db_svm_cf_ov_acc - nb_tda_kde_5.60.5_n5_db_nb_cf0_ov_acc)
#diff_drybean_tda_kde_5.60.5_nb.n5_test
## Bayesian Tests Test set diff
# Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n5_test<-BayesianSignTest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n5_test),-0.01,0.01)
#bst_tda_kde_5.60.5_nb.n5_test
# Odds Left Bayesian Sign Test
#bst_tda_kde_5.60.5_nb.n5_test_odds.left<-bst_tda_kde_5.60.5_nb.n5_test$probLeft/#bst_tda_kde_5.60.5_nb.n5_test$probRight
#bst_tda_kde_5.60.5_nb.n5_test_odds.left
# Bayesian Signed Rank Test
#bsr_tda_kde_5.60.5_nb.n5_test<-#BayesianSignedRank(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n5_test),-0.01,0.01)
#bsr_tda_kde_5.60.5_nb.n5_test
# Bayesian Correlated Test
#bct_tda_kde_5.60.5_nb.n5_test<-#correlatedBayesianTtest(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n5_test),0.1,-0.01,0.01)
#bct_tda_kde_5.60.5_nb.n5_test
# Rope Plot
#plot(rope(diff_drybean_tda_kde_5.60.5_nb.n5_test)))
#BayesFactor
#bf_tda_kde_5.60.5_nb.n5_test = ttestBF(x = as.matrix(diff_drybean_tda_kde_5.60.5_nb.n5_test)) #bf_tda_kde_5.60.5_nb.n5_test
#t_test
#t.test(as.matrix(diff_drybean_tda_kde_5.60.5_nb.n5_test))